In [1]:
import tensorflow as tf
print(tf.__version__)
1.15.0
Tensor is Just a Fancy word of N Dimensional Array
In [2]:
hello = tf.constant("Hello ")
world = tf.constant("World")
In [3]:
type(hello) #These is a Tensor Object
Out[3]:
tensorflow.python.framework.ops.Tensor
In [4]:
print(hello)
Tensor("Const:0", shape=(), dtype=string)
In [5]:
# To Print Hello we need to run these inside a Session
with tf.Session() as sess: #with - Automatically closes the session once block of code is executed
result = sess.run(hello + world)
In [6]:
print(result) #b respresents bytes literal
b'Hello World'
In [7]:
# We will also try addtion
a = tf.constant(10)
b = tf.constant(20)
type(a)
Out[7]:
tensorflow.python.framework.ops.Tensor
In [8]:
a + b #You will not get the output - You need to run these inside a session
Out[8]:
<tf.Tensor 'add_1:0' shape=() dtype=int32>
In [9]:
with tf.Session() as sess:
result = sess.run(a+b)
In [10]:
print(result)
30
In [11]:
const = tf.constant(10)
fill_mat = tf.fill((4,4),10)
fill_mat
Out[11]:
<tf.Tensor 'Fill:0' shape=(4, 4) dtype=int32>
In [12]:
myzeros = tf.zeros((4,4)) # Tensor or ndarray with elements set to 0
In [13]:
myones = tf.ones((4,4))
In [14]:
myrandn = tf.add_nrandom_normal((4,4),mean=0,stddev = 1.0)
In [15]:
myrandu = tf.random_uniform((4,4),minval = 0,maxval=1)
In [16]:
my_ops = [const,fill_mat,myzeros,myones,myrandn,myrandu]
In [17]:
#We can use Interactice Session
sess = tf.InteractiveSession()
#Now rest of the cells will be called as you are executing - with tf.Session() as sess:
In [18]:
for op in my_ops:
print(sess.run(op)) #Instead of sess.run(op) - you can also write op.val
print('\n')
10 [[10 10 10 10] [10 10 10 10] [10 10 10 10] [10 10 10 10]] [[0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.]] [[1. 1. 1. 1.] [1. 1. 1. 1.] [1. 1. 1. 1.] [1. 1. 1. 1.]] [[ 0.89284676 2.3945236 -0.21507755 0.22809415] [ 0.78802454 0.29111588 0.08790298 -0.13657601] [-0.18791787 2.5887072 -0.01814605 0.86429554] [ 0.5102834 0.3856326 -1.4145305 -0.36906108]] [[0.32991064 0.5717 0.53629947 0.29625154] [0.6961372 0.8492203 0.15640211 0.6441988 ] [0.61591864 0.37401402 0.13195145 0.9395678 ] [0.01590729 0.3868184 0.10500526 0.9902581 ]]
In [21]:
a = tf.constant([ [1,2],
[3,4]
])
print(a)
a.eval()
Tensor("Const_7:0", shape=(2, 2), dtype=int32)
Out[21]:
array([[1, 2],
[3, 4]])
In [22]:
a.get_shape()
Out[22]:
TensorShape([Dimension(2), Dimension(2)])
In [23]:
b = tf.constant([[10],[100]])
In [24]:
b.get_shape()
Out[24]:
TensorShape([Dimension(2), Dimension(1)])
In [25]:
result = tf.matmul(a,b)
sess.run(result)
Out[25]:
array([[210],
[430]])
In [26]:
result.eval()
Out[26]:
array([[210],
[430]])
Tensorflow Graphs¶
In [27]:
n1 = tf.constant(1)
n2 = tf.constant(2)
n3 = n1+n2 #Operation Node n3
In [28]:
with tf.Session() as sess:
result = sess.run(n3)
In [29]:
print(result)
3
In [30]:
print(n3) #Without running in session
Tensor("add_3:0", shape=(), dtype=int32)
When you start tensorflow a default graph is created and you can create additional graphs
In [31]:
print(tf.get_default_graph())
<tensorflow.python.framework.ops.Graph object at 0x0000014476CA7940>
In [32]:
g = tf.Graph()
In [33]:
print(g)
<tensorflow.python.framework.ops.Graph object at 0x00000145080E2828>
In [34]:
#Now we want to set this new graph as default graph
graph_one = tf.get_default_graph()
graph_one
Out[34]:
<tensorflow.python.framework.ops.Graph at 0x14476ca7940>
In [35]:
graph_two = tf.Graph()
graph_two
Out[35]:
<tensorflow.python.framework.ops.Graph at 0x1450884d320>
In [36]:
#Set graph_two as default graph
with graph_two.as_default():
print(graph_two is tf.get_default_graph())
True
In [37]:
#Now when we run it at outside that with - False
print(graph_two is tf.get_default_graph())
False
Variables and Place Holders¶
In [39]:
sess.close()
In [40]:
sess = tf.InteractiveSession()
In [41]:
my_tensor = tf.random_uniform((4,4),0,1)
In [42]:
my_tensor
Out[42]:
<tf.Tensor 'random_uniform_1:0' shape=(4, 4) dtype=float32>
In [43]:
my_var = tf.Variable(initial_value=my_tensor)
In [44]:
print(my_var)
<tf.Variable 'Variable:0' shape=(4, 4) dtype=float32_ref>
In [45]:
sess.run(my_var) #You will get error , Because we need to initialize our varaible - It is neccessary to run Initialize code
--------------------------------------------------------------------------- FailedPreconditionError Traceback (most recent call last) ~\Anaconda3\lib\site-packages\tensorflow_core\python\client\session.py in _do_call(self, fn, *args) 1364 try: -> 1365 return fn(*args) 1366 except errors.OpError as e: ~\Anaconda3\lib\site-packages\tensorflow_core\python\client\session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata) 1349 return self._call_tf_sessionrun(options, feed_dict, fetch_list, -> 1350 target_list, run_metadata) 1351 ~\Anaconda3\lib\site-packages\tensorflow_core\python\client\session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata) 1442 fetch_list, target_list, -> 1443 run_metadata) 1444 FailedPreconditionError: 2 root error(s) found. (0) Failed precondition: Attempting to use uninitialized value Variable [[{{node Variable}}]] [[Variable/_1]] (1) Failed precondition: Attempting to use uninitialized value Variable [[{{node Variable}}]] 0 successful operations. 0 derived errors ignored. During handling of the above exception, another exception occurred: FailedPreconditionError Traceback (most recent call last) <ipython-input-45-cb56b9f5458c> in <module> ----> 1 sess.run(my_var) #You will get error , Because we need to initialize our varaible ~\Anaconda3\lib\site-packages\tensorflow_core\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata) 954 try: 955 result = self._run(None, fetches, feed_dict, options_ptr, --> 956 run_metadata_ptr) 957 if run_metadata: 958 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr) ~\Anaconda3\lib\site-packages\tensorflow_core\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata) 1178 if final_fetches or final_targets or (handle and feed_dict_tensor): 1179 results = self._do_run(handle, final_targets, final_fetches, -> 1180 feed_dict_tensor, options, run_metadata) 1181 else: 1182 results = [] ~\Anaconda3\lib\site-packages\tensorflow_core\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata) 1357 if handle is None: 1358 return self._do_call(_run_fn, feeds, fetches, targets, options, -> 1359 run_metadata) 1360 else: 1361 return self._do_call(_prun_fn, handle, feeds, fetches) ~\Anaconda3\lib\site-packages\tensorflow_core\python\client\session.py in _do_call(self, fn, *args) 1382 '\nsession_config.graph_options.rewrite_options.' 1383 'disable_meta_optimizer = True') -> 1384 raise type(e)(node_def, op, message) 1385 1386 def _extend_graph(self): FailedPreconditionError: 2 root error(s) found. (0) Failed precondition: Attempting to use uninitialized value Variable [[node Variable (defined at C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\framework\ops.py:1748) ]] [[Variable/_1]] (1) Failed precondition: Attempting to use uninitialized value Variable [[node Variable (defined at C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\framework\ops.py:1748) ]] 0 successful operations. 0 derived errors ignored. Original stack trace for 'Variable': File "C:\Users\Lucky_Rathod\Anaconda3\lib\runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) File "C:\Users\Lucky_Rathod\Anaconda3\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\ipykernel_launcher.py", line 16, in <module> app.launch_new_instance() File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance app.start() File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 499, in start self.io_loop.start() File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 148, in start self.asyncio_loop.run_forever() File "C:\Users\Lucky_Rathod\Anaconda3\lib\asyncio\base_events.py", line 539, in run_forever self._run_once() File "C:\Users\Lucky_Rathod\Anaconda3\lib\asyncio\base_events.py", line 1775, in _run_once handle._run() File "C:\Users\Lucky_Rathod\Anaconda3\lib\asyncio\events.py", line 88, in _run self._context.run(self._callback, *self._args) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 138, in _handle_events handler_func(fileobj, events) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 456, in _handle_events self._handle_recv() File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 486, in _handle_recv self._run_callback(callback, msg) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 438, in _run_callback callback(*args, **kwargs) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher return self.dispatch_shell(stream, msg) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 233, in dispatch_shell handler(stream, idents, msg) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request user_expressions, allow_stdin) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 208, in do_execute res = shell.run_cell(code, store_history=store_history, silent=silent) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 537, in run_cell return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2854, in run_cell raw_cell, store_history, silent, shell_futures) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2880, in _run_cell return runner(coro) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\IPython\core\async_helpers.py", line 68, in _pseudo_sync_runner coro.send(None) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3057, in run_cell_async interactivity=interactivity, compiler=compiler, result=result) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3248, in run_ast_nodes if (await self.run_code(code, result, async_=asy)): File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3325, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-43-4e22ea9c99a4>", line 1, in <module> my_var = tf.Variable(initial_value=my_tensor) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\variables.py", line 258, in __call__ return cls._variable_v1_call(*args, **kwargs) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\variables.py", line 219, in _variable_v1_call shape=shape) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\variables.py", line 197, in <lambda> previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\variable_scope.py", line 2519, in default_variable_creator shape=shape) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\variables.py", line 262, in __call__ return super(VariableMetaclass, cls).__call__(*args, **kwargs) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\variables.py", line 1688, in __init__ shape=shape) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\variables.py", line 1846, in _init_from_args shape, self._initial_value.dtype.base_dtype, name=name) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\state_ops.py", line 79, in variable_op_v2 shared_name=shared_name) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\gen_state_ops.py", line 1621, in variable_v2 shared_name=shared_name, name=name) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\framework\op_def_library.py", line 794, in _apply_op_helper op_def=op_def) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 507, in new_func return func(*args, **kwargs) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\framework\ops.py", line 3357, in create_op attrs, op_def, compute_device) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\framework\ops.py", line 3426, in _create_op_internal op_def=op_def) File "C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1748, in __init__ self._traceback = tf_stack.extract_stack()
In [46]:
init = tf.global_variables_initializer()
In [47]:
sess.run(init) #You need to run these init first then run your varaibles
In [48]:
sess.run(my_tensor)
Out[48]:
array([[0.79889846, 0.08088684, 0.33100903, 0.87996805],
[0.24146831, 0.9800799 , 0.4856255 , 0.14611554],
[0.786464 , 0.2716161 , 0.598897 , 0.8662473 ],
[0.99681354, 0.7854526 , 0.8559971 , 0.14767623]], dtype=float32)
Placeholder¶
In [49]:
ph = tf.placeholder(tf.float32)
In [50]:
ph1 = tf.placeholder(tf.float32,shape=(None,5)) #None - Will be replace to Samples in data
Tensorflow Neural Network - Part 1¶
We will create a neuron that performs very simple linear fit to some 2D data Steps : 1.Build Graph 2.Initiate Session 3.Feed Data In and get Output
Afterwards we can add Cost Function in order to train your network to optimize the parameters
In [51]:
sess.close
Out[51]:
<bound method InteractiveSession.close of <tensorflow.python.client.session.InteractiveSession object at 0x00000145088F2128>>
In [52]:
sess.close()
In [53]:
import numpy as np
np.random.seed(101)
tf.set_random_seed(101)
In [54]:
rand_a = np.random.uniform(0,100,(5,5))
rand_a
Out[54]:
array([[51.63986277, 57.06675869, 2.84742265, 17.15216562, 68.52769817],
[83.38968626, 30.69662197, 89.36130797, 72.15438618, 18.99389542],
[55.42275911, 35.2131954 , 18.18924027, 78.56017619, 96.54832224],
[23.23536618, 8.35614337, 60.35484223, 72.89927573, 27.62388285],
[68.53063288, 51.78674742, 4.84845374, 13.78692376, 18.69674261]])
In [55]:
rand_b = np.random.uniform(0,100,(5,1))
rand_b
Out[55]:
array([[99.43179012],
[52.06653967],
[57.87895355],
[73.48190583],
[54.19617722]])
In [56]:
#PlaceHolders
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
In [57]:
#Operations
add_op = a + b
mul_op = a * b
In [58]:
#Sessions
with tf.Session() as sess:
# a and b are placeholder object they need data
add_result = sess.run(add_op,feed_dict={a:10,b:20})
print(add_result)
30.0
In [59]:
with tf.Session() as sess:
# a and b are placeholder object they need data
add_result = sess.run(add_op,feed_dict={a:rand_a,b:rand_b})
print(add_result)
[[151.07166 156.49855 102.27921 116.58396 167.95949 ] [135.45622 82.76316 141.42784 124.22093 71.06043 ] [113.30171 93.09215 76.06819 136.43912 154.42728 ] [ 96.71727 81.83804 133.83675 146.38118 101.10579 ] [122.72681 105.982925 59.044632 67.9831 72.89292 ]]
In [60]:
with tf.Session() as sess:
# a and b are placeholder object they need data
add_result = sess.run(add_op,feed_dict={a:rand_a,b:rand_b})
print(add_result)
print('\n')
mul_result = sess.run(mul_op,feed_dict={a:rand_a,b:rand_b})
print(mul_result)
[[151.07166 156.49855 102.27921 116.58396 167.95949 ] [135.45622 82.76316 141.42784 124.22093 71.06043 ] [113.30171 93.09215 76.06819 136.43912 154.42728 ] [ 96.71727 81.83804 133.83675 146.38118 101.10579 ] [122.72681 105.982925 59.044632 67.9831 72.89292 ]] [[5134.644 5674.25 283.12433 1705.4707 6813.8315 ] [4341.8125 1598.267 4652.734 3756.8293 988.94635] [3207.8113 2038.1029 1052.7742 4546.9805 5588.1157 ] [1707.379 614.02527 4434.989 5356.7773 2029.8555 ] [3714.0984 2806.6438 262.76764 747.19855 1013.292 ]]
In [61]:
# Constant Varaible Names
n_features = 10
n_dense_neurons = 3 #1 layer of 3 dense neurons
In [62]:
# Placeholders
x = tf.placeholder(tf.float32,shape=(None,n_features)) #We dont how much rows will be provided , But we know no of columns
In [63]:
W = tf.Variable(tf.random_normal([n_features,n_dense_neurons])) #Weights
b = tf.Variable(tf.ones([n_dense_neurons])) # W will be multiplied by x but in these we are just adding
In [65]:
#Operations and Activation Function
xW = tf.matmul(x,W)
z = tf.add(xW,b)
a = tf.sigmoid(z)
In [67]:
# Now our Neural Network also has Variables .So we need to first Run our tf_global_varaibles_initializer
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
layer_out = sess.run(a,feed_dict={x:np.random.random([1,n_features])})
In [68]:
print(layer_out)
[[0.85899407 0.926714 0.45346332]]
Till Now we are not adjusting values of W and b. \ We basically choose random values of W and b.And run it them and see what happens out. \ That is not how we run a neural network.We need to go back and adjust W and b based on some sort of Cost function. \ So we need Cost Function and Optimizer to adjust W and b
Simple Linear Regression Example¶
In [69]:
x_data = np.linspace(0,10,10) + np.random.uniform(-1.5,1.5,10) #10 Linearly spaced points between 0 and 10 and then add little bit noise to it
In [70]:
x_data
Out[70]:
array([-1.20856056, -0.08034641, 2.82674411, 4.50477294, 3.42312535,
4.88227319, 7.18414126, 6.77068715, 9.4930023 , 9.96290567])
In [71]:
y_label = np.linspace(0,10,10) + np.random.uniform(-1.5,1.5,10)
y_label
Out[71]:
array([0.5420333 , 1.17575569, 0.85241231, 2.50514314, 4.67005971,
4.41685654, 6.66701681, 6.69180648, 7.54731409, 9.03483077])
In [74]:
import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(x_data,y_label,'*')
Out[74]:
[<matplotlib.lines.Line2D at 0x1450bcacac8>]
Now we will start our Neural Network with Graphs¶
y = mx + b
In [75]:
np.random.rand(2)
#These will give me 2 random values and Im going to initialize M and B to be random values
Out[75]:
array([0.44236813, 0.87758732])
In [76]:
m = tf.Variable(0.44)
b = tf.Variable(0.87)
#It is upto neural network with cost function and optimizer to fix these values to create Fitted Line in Above Figure
In [77]:
# Cost Function
error = 0
for x,y in zip(x_data,y_label): #These will make List of Tuples of each point in X and corresponding y label
y_hat = m*x + b
#Now y_hat will be totally random because we have random values in m and b
#Cost Function
error += (y-y_hat)**2
#Now in order to minimize this we need to use some sort of optimizer
In [78]:
#Optimizer to minimize the cost function
optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.001)
train = optimizer.minimize(error)
WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\math_grad.py:1375: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where
In [79]:
# Run it in Session by first running init
init = tf.global_variables_initializer()
In [80]:
with tf.Session() as sess:
sess.run(init)
training_steps = 1
for i in range(training_steps):
sess.run(train)
#These will give us final weights after the network has learned
final_slope , final_intercept = sess.run([m,b])
In [81]:
x_test = np.linspace(-1,11,10)
#y = mx +b
y_pred_plot = final_slope * x_test + final_intercept
plt.plot(x_test,y_pred_plot,'r')
plt.plot(x_data,y_label,'*')
Out[81]:
[<matplotlib.lines.Line2D at 0x1450925c390>]
In [82]:
# Now we will do these for 100 training steps
with tf.Session() as sess:
sess.run(init)
training_steps = 100
for i in range(training_steps):
sess.run(train)
#These will give us final weights after the network has learned
final_slope , final_intercept = sess.run([m,b])
In [83]:
x_test = np.linspace(-1,11,10)
#y = mx +b
y_pred_plot = final_slope * x_test + final_intercept
plt.plot(x_test,y_pred_plot,'r')
plt.plot(x_data,y_label,'*')
Out[83]:
[<matplotlib.lines.Line2D at 0x1450fb58be0>]
Tensorflow Regression Example - Part 1¶
In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
In [2]:
#Dataset
x_data = np.linspace(0.0,10.0,1000000)
noise = np.random.randn(len(x_data))
print(x_data)
print('\n')
print(noise)
[0.000000e+00 1.000001e-05 2.000002e-05 ... 9.999980e+00 9.999990e+00 1.000000e+01] [ 1.87075392 -1.01748648 -0.4538662 ... 0.18139723 -1.160871 -0.82472978]
y = mx + b \ b = 5
In [3]:
#Now m = 0.5 which we will discover from our model
y_true = (0.5 * x_data) + 5 + noise
In [7]:
# Using Pandas
x_df = pd.DataFrame(data=x_data,columns = ['X Data'])
x_df.head()
Out[7]:
| X Data | |
|---|---|
| 0 | 0.00000 |
| 1 | 0.00001 |
| 2 | 0.00002 |
| 3 | 0.00003 |
| 4 | 0.00004 |
In [8]:
y_df = pd.DataFrame(data = y_true,columns=['Y'])
y_df.head()
Out[8]:
| Y | |
|---|---|
| 0 | 6.870754 |
| 1 | 3.982519 |
| 2 | 4.546144 |
| 3 | 6.980582 |
| 4 | 4.212941 |
In [9]:
my_data = pd.concat([x_df,y_df],axis=1)
my_data.head()
Out[9]:
| X Data | Y | |
|---|---|---|
| 0 | 0.00000 | 6.870754 |
| 1 | 0.00001 | 3.982519 |
| 2 | 0.00002 | 4.546144 |
| 3 | 0.00003 | 6.980582 |
| 4 | 0.00004 | 4.212941 |
In [11]:
# Plotting sample of my_data
my_data.sample(n=250).head()
Out[11]:
| X Data | Y | |
|---|---|---|
| 635800 | 6.358006 | 6.784782 |
| 877646 | 8.776469 | 9.911813 |
| 803517 | 8.035178 | 9.471129 |
| 284489 | 2.844893 | 5.082467 |
| 575724 | 5.757246 | 8.061628 |
In [14]:
my_data.sample(n=250).plot(kind='scatter',x='X Data',y='Y');
Now we want tensorflow to fit a Line in above plot. \ Now we cant feed in 1000000 points at a time in Neural network.So it is neccesary for you to create batches of data
In [15]:
batch_size = 8 #We will grab 8 points from 1000000 at a time
In [29]:
# Now we take random values for m and b
np.random.randn(2)
Out[29]:
array([0.80396029, 1.22133395])
In [17]:
# Create m and b variables
m = tf.Variable(0.81)
b = tf.Variable(0.87)
In [30]:
#Placeholders for x and y
xph = tf.placeholder(tf.float32,[batch_size])
yph = tf.placeholder(tf.float32,[batch_size])
In [31]:
# Graph - Operations that we will perform
y_model = m * xph + b
#Cost Function
error = tf.reduce_sum(tf.square(yph - y_model))
#Optimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.001)
train = optimizer.minimize(error)
In [32]:
# Init Global Variables
init = tf.global_variables_initializer()
In [33]:
rand_indtest = np.random.randint(len(x_data),size = batch_size)
rand_indtest
Out[33]:
array([396712, 719424, 758756, 395672, 164960, 973728, 224381, 638525])
In [34]:
#Session
with tf.Session() as sess:
sess.run(init)
batches =1000
#We will feed in 1000 batches of Data where each batch has 8 Corresponding data
for i in range(batches):
rand_ind = np.random.randint(len(x_data),size = batch_size)
feed = {xph:x_data[rand_ind],yph:y_true[rand_ind]}
sess.run(train,feed_dict=feed)
#We will grab final value of m and b
model_m , model_b = sess.run([m,b])
In [35]:
#We know that true slope should be close to 0.5 - We Initiall started with 0.81 and b - 0.87
print(model_m) #m - 0.5
print(model_b) #b - 5
0.4857926 4.875115
In [37]:
#Visualize Results
y_hat = x_data*model_m + model_b
my_data.sample(250).plot(kind='scatter',x='X Data',y='Y')
plt.plot(x_data,y_hat,'r')
Out[37]:
[<matplotlib.lines.Line2D at 0x1478f93ecf8>]
Tensorflow Estimator API - Regression Task¶
Steps for using Estimator API¶
1.Define List of Feature Columns \ 2.Create Estimator Model \ 3.Create Data Input Function (Takes data as numpy array) \ 4.Call train,evaluate and predict methods on estimator object
In [38]:
# Step 1 - Features Column list for Estimator
feat_cols = [ tf.feature_column.numeric_column('x',shape=[1]) ] #Shape we have only 1 Dimension
In [39]:
# Step 2 - Create Estimator Model
estimator = tf.estimator.LinearRegressor(feature_columns=feat_cols)
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: C:\Users\LUCKY_~1\AppData\Local\Temp\tmpfczr2zsc
INFO:tensorflow:Using config: {'_model_dir': 'C:\\Users\\LUCKY_~1\\AppData\\Local\\Temp\\tmpfczr2zsc', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x000001479EE532B0>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
In [41]:
x_data
Out[41]:
array([0.000000e+00, 1.000001e-05, 2.000002e-05, ..., 9.999980e+00,
9.999990e+00, 1.000000e+01])
In [42]:
# Train Test split from sklearn
from sklearn.model_selection import train_test_split
x_train,x_eval,y_train,y_eval = train_test_split(x_data,y_true,test_size=0.3,random_state=101)
print(x_train.shape)
print(x_eval.shape)
print(y_train.shape)
print(y_eval.shape)
(700000,) (300000,) (700000,) (300000,)
In [43]:
# Step 3 - Create Data Input Function - It acts like your Feed Dictionary,Batch Size
#We will pass same feature column name that we wrote in step 1
input_func = tf.estimator.inputs.numpy_input_fn({'x':x_train},y_train,
batch_size = 8,
num_epochs=None,
shuffle=True)
#Train input Function
train_input_func = tf.estimator.inputs.numpy_input_fn({'x':x_train},y_train,
batch_size = 8,
num_epochs=1000,
shuffle=False) #Using Train input function for evaluation against a test input function
#Train input Function
eval_input_func = tf.estimator.inputs.numpy_input_fn({'x':x_eval},y_eval,
batch_size = 8,
num_epochs=1000,
shuffle=False)
In [44]:
# Step 4
#Train Estimator
estimator.train(input_fn=input_func,steps=1000) #We didnt specified Epochs for input_func above,we will specify here
WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\training\training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\inputs\queues\feeding_queue_runner.py:62: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the `tf.data` module. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\inputs\queues\feeding_functions.py:500: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the `tf.data` module. INFO:tensorflow:Calling model_fn. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\feature_column\feature_column_v2.py:305: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version. Instructions for updating: Please use `layer.add_weight` method instead. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version. Instructions for updating: If using Keras pass *_constraint arguments to layers. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\linear.py:308: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\array_ops.py:1475: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\training\monitored_session.py:882: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the `tf.data` module. INFO:tensorflow:Saving checkpoints for 0 into C:\Users\LUCKY_~1\AppData\Local\Temp\tmpfczr2zsc\model.ckpt. INFO:tensorflow:loss = 329.7203, step = 0 INFO:tensorflow:global_step/sec: 108.987 INFO:tensorflow:loss = 41.362682, step = 100 (0.893 sec) INFO:tensorflow:global_step/sec: 127.567 INFO:tensorflow:loss = 33.252625, step = 200 (0.786 sec) INFO:tensorflow:global_step/sec: 134.768 INFO:tensorflow:loss = 14.118053, step = 300 (0.740 sec) WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 334 vs previous value: 334. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize. INFO:tensorflow:global_step/sec: 138.875 INFO:tensorflow:loss = 8.768937, step = 400 (0.718 sec) INFO:tensorflow:global_step/sec: 174.681 INFO:tensorflow:loss = 7.899567, step = 500 (0.572 sec) INFO:tensorflow:global_step/sec: 205.469 INFO:tensorflow:loss = 18.130033, step = 600 (0.486 sec) INFO:tensorflow:global_step/sec: 200.937 INFO:tensorflow:loss = 6.0403175, step = 700 (0.498 sec) INFO:tensorflow:global_step/sec: 261.114 INFO:tensorflow:loss = 6.926654, step = 800 (0.383 sec) INFO:tensorflow:global_step/sec: 272.466 INFO:tensorflow:loss = 9.865296, step = 900 (0.367 sec) INFO:tensorflow:Saving checkpoints for 1000 into C:\Users\LUCKY_~1\AppData\Local\Temp\tmpfczr2zsc\model.ckpt. INFO:tensorflow:Loss for final step: 5.4456644.
Out[44]:
<tensorflow_estimator.python.estimator.canned.linear.LinearRegressor at 0x1479ee4e240>
In [45]:
# Evaluation Metrics
train_metrics = estimator.evaluate(input_fn=train_input_func,steps=1000)
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2020-08-19T14:07:06Z INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\LUCKY_~1\AppData\Local\Temp\tmpfczr2zsc\model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [100/1000] INFO:tensorflow:Evaluation [200/1000] INFO:tensorflow:Evaluation [300/1000] INFO:tensorflow:Evaluation [400/1000] INFO:tensorflow:Evaluation [500/1000] INFO:tensorflow:Evaluation [600/1000] INFO:tensorflow:Evaluation [700/1000] INFO:tensorflow:Evaluation [800/1000] INFO:tensorflow:Evaluation [900/1000] INFO:tensorflow:Evaluation [1000/1000] INFO:tensorflow:Finished evaluation at 2020-08-19-14:07:36 INFO:tensorflow:Saving dict for global step 1000: average_loss = 1.061466, global_step = 1000, label/mean = 7.513248, loss = 8.491728, prediction/mean = 7.33972 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: C:\Users\LUCKY_~1\AppData\Local\Temp\tmpfczr2zsc\model.ckpt-1000
In [46]:
eval_metrics = estimator.evaluate(input_fn=eval_input_func,steps=1000)
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2020-08-19T14:08:39Z INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\LUCKY_~1\AppData\Local\Temp\tmpfczr2zsc\model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [100/1000] INFO:tensorflow:Evaluation [200/1000] INFO:tensorflow:Evaluation [300/1000] INFO:tensorflow:Evaluation [400/1000] INFO:tensorflow:Evaluation [500/1000] INFO:tensorflow:Evaluation [600/1000] INFO:tensorflow:Evaluation [700/1000] INFO:tensorflow:Evaluation [800/1000] INFO:tensorflow:Evaluation [900/1000] INFO:tensorflow:Evaluation [1000/1000] INFO:tensorflow:Finished evaluation at 2020-08-19-14:08:47 INFO:tensorflow:Saving dict for global step 1000: average_loss = 1.1022389, global_step = 1000, label/mean = 7.480234, loss = 8.817911, prediction/mean = 7.3190165 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: C:\Users\LUCKY_~1\AppData\Local\Temp\tmpfczr2zsc\model.ckpt-1000
In [48]:
print('Training Data Metrics')
print(train_metrics)
Training Data Metrics
{'average_loss': 1.061466, 'label/mean': 7.513248, 'loss': 8.491728, 'prediction/mean': 7.33972, 'global_step': 1000}
In [49]:
print('Evaluation Metrics')
print(eval_metrics)
Evaluation Metrics
{'average_loss': 1.1022389, 'label/mean': 7.480234, 'loss': 8.817911, 'prediction/mean': 7.3190165, 'global_step': 1000}
In [50]:
# prediction of new values
brand_new_data = np.linspace(0,10,10)
input_fn_predict = tf.estimator.inputs.numpy_input_fn({'x':brand_new_data},shuffle=False)
estimator.predict(input_fn=input_fn_predict)
Out[50]:
<generator object Estimator.predict at 0x00000147A0F66570>
In [51]:
#Convert Generator to list
list(estimator.predict(input_fn=input_fn_predict))
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\LUCKY_~1\AppData\Local\Temp\tmpfczr2zsc\model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op.
Out[51]:
[{'predictions': array([4.473729], dtype=float32)},
{'predictions': array([5.1103287], dtype=float32)},
{'predictions': array([5.746928], dtype=float32)},
{'predictions': array([6.3835278], dtype=float32)},
{'predictions': array([7.0201273], dtype=float32)},
{'predictions': array([7.656727], dtype=float32)},
{'predictions': array([8.293326], dtype=float32)},
{'predictions': array([8.929926], dtype=float32)},
{'predictions': array([9.566526], dtype=float32)},
{'predictions': array([10.203125], dtype=float32)}]
In [52]:
predictions = []
for pred in estimator.predict(input_fn=input_fn_predict):
predictions.append(pred['predictions'])
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\LUCKY_~1\AppData\Local\Temp\tmpfczr2zsc\model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op.
In [53]:
predictions
Out[53]:
[array([4.473729], dtype=float32), array([5.1103287], dtype=float32), array([5.746928], dtype=float32), array([6.3835278], dtype=float32), array([7.0201273], dtype=float32), array([7.656727], dtype=float32), array([8.293326], dtype=float32), array([8.929926], dtype=float32), array([9.566526], dtype=float32), array([10.203125], dtype=float32)]
In [54]:
my_data.sample(250).plot(kind='scatter',x='X Data',y='Y')
plt.plot(brand_new_data,predictions,'r*')
Out[54]:
[<matplotlib.lines.Line2D at 0x1489a1ca8d0>]
In [1]:
# Import Diabetes Data set
import pandas as pd
diabetes = pd.read_csv(r'C:\Users\Lucky_Rathod\Desktop\Udemy Courses\Complete Tensorflow\Original Notes\Tensorflow-Bootcamp-master\02-TensorFlow-Basics\pima-indians-diabetes.csv')
diabetes.head() #1 - They HAVE DIABETES , 0 - THEY DONT HAVE DIABETES
Out[1]:
| Number_pregnant | Glucose_concentration | Blood_pressure | Triceps | Insulin | BMI | Pedigree | Age | Class | Group | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6 | 0.743719 | 0.590164 | 0.353535 | 0.000000 | 0.500745 | 0.234415 | 50 | 1 | B |
| 1 | 1 | 0.427136 | 0.540984 | 0.292929 | 0.000000 | 0.396423 | 0.116567 | 31 | 0 | C |
| 2 | 8 | 0.919598 | 0.524590 | 0.000000 | 0.000000 | 0.347243 | 0.253629 | 32 | 1 | B |
| 3 | 1 | 0.447236 | 0.540984 | 0.232323 | 0.111111 | 0.418778 | 0.038002 | 21 | 0 | B |
| 4 | 0 | 0.688442 | 0.327869 | 0.353535 | 0.198582 | 0.642325 | 0.943638 | 33 | 1 | C |
In [2]:
# Clean or Normalize Dataset
diabetes.columns
Out[2]:
Index(['Number_pregnant', 'Glucose_concentration', 'Blood_pressure', 'Triceps',
'Insulin', 'BMI', 'Pedigree', 'Age', 'Class', 'Group'],
dtype='object')
In [3]:
cols_to_norm = ['Number_pregnant', 'Glucose_concentration', 'Blood_pressure', 'Triceps',
'Insulin', 'BMI', 'Pedigree']
In [6]:
diabetes[cols_to_norm].head()
Out[6]:
| Number_pregnant | Glucose_concentration | Blood_pressure | Triceps | Insulin | BMI | Pedigree | |
|---|---|---|---|---|---|---|---|
| 0 | 6 | 0.743719 | 0.590164 | 0.353535 | 0.000000 | 0.500745 | 0.234415 |
| 1 | 1 | 0.427136 | 0.540984 | 0.292929 | 0.000000 | 0.396423 | 0.116567 |
| 2 | 8 | 0.919598 | 0.524590 | 0.000000 | 0.000000 | 0.347243 | 0.253629 |
| 3 | 1 | 0.447236 | 0.540984 | 0.232323 | 0.111111 | 0.418778 | 0.038002 |
| 4 | 0 | 0.688442 | 0.327869 | 0.353535 | 0.198582 | 0.642325 | 0.943638 |
In [7]:
#Normalizing all above columns
diabetes[cols_to_norm] = diabetes[cols_to_norm].apply(lambda x : (x - x.min()) / (x.max() - x.min()))
diabetes.head()
Out[7]:
| Number_pregnant | Glucose_concentration | Blood_pressure | Triceps | Insulin | BMI | Pedigree | Age | Class | Group | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.352941 | 0.743719 | 0.590164 | 0.353535 | 0.000000 | 0.500745 | 0.234415 | 50 | 1 | B |
| 1 | 0.058824 | 0.427136 | 0.540984 | 0.292929 | 0.000000 | 0.396423 | 0.116567 | 31 | 0 | C |
| 2 | 0.470588 | 0.919598 | 0.524590 | 0.000000 | 0.000000 | 0.347243 | 0.253629 | 32 | 1 | B |
| 3 | 0.058824 | 0.447236 | 0.540984 | 0.232323 | 0.111111 | 0.418778 | 0.038002 | 21 | 0 | B |
| 4 | 0.000000 | 0.688442 | 0.327869 | 0.353535 | 0.198582 | 0.642325 | 0.943638 | 33 | 1 | C |
Binary Classification - Tensorflow¶
In [8]:
import tensorflow as tf
In [9]:
diabetes.columns
Out[9]:
Index(['Number_pregnant', 'Glucose_concentration', 'Blood_pressure', 'Triceps',
'Insulin', 'BMI', 'Pedigree', 'Age', 'Class', 'Group'],
dtype='object')
In [10]:
# Step 1 - Create Feature columns list
# Numeric Features
num_preg = tf.feature_column.numeric_column('Number_pregnant')
plasma_gluc = tf.feature_column.numeric_column('Glucose_concentration')
dias_press = tf.feature_column.numeric_column('Blood_pressure')
tricep = tf.feature_column.numeric_column('Triceps')
insulin = tf.feature_column.numeric_column('Insulin')
bmi = tf.feature_column.numeric_column('BMI')
diabetes_pedigree = tf.feature_column.numeric_column('Pedigree')
age = tf.feature_column.numeric_column('Age')
# Categroical Features
## Method 1 - Identify no of groups - Usefull when categories are less
assigned_group = tf.feature_column.categorical_column_with_vocabulary_list('Group',['A','B','C','D'])
## Method 2 - Usefull when there are more categories - Eg - Countries in World
# hash_bucket_size - Max no of categroies you will have in that column
#assigned_group = tf.feature_column.categorical_column_with_hash_bucket('Group',hash_bucket_size=10)
In [11]:
# Converting Continous Column to Categorical Column
# We have not Normalized 'Age' because we will convert it into categorical column
import matplotlib.pyplot as plt
%matplotlib inline
diabetes['Age'].hist(bins=20);
In [12]:
#We can create buckets for Decades
#In this you need to pass continous Feature which you created in step1 - age
#Converting Continous Numeric to Categorical Column
age_bucket = tf.feature_column.bucketized_column(age,boundaries=[20,30,40,50,60,70,80])
In [13]:
age_bucket
Out[13]:
BucketizedColumn(source_column=NumericColumn(key='Age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), boundaries=(20, 30, 40, 50, 60, 70, 80))
In [14]:
feat_cols = [num_preg,plasma_gluc,dias_press,tricep,insulin,bmi,diabetes_pedigree,assigned_group,age_bucket]
In [15]:
# Train Test Split
X_data = diabetes.drop('Class',axis=1)
X_data.head()
Out[15]:
| Number_pregnant | Glucose_concentration | Blood_pressure | Triceps | Insulin | BMI | Pedigree | Age | Group | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.352941 | 0.743719 | 0.590164 | 0.353535 | 0.000000 | 0.500745 | 0.234415 | 50 | B |
| 1 | 0.058824 | 0.427136 | 0.540984 | 0.292929 | 0.000000 | 0.396423 | 0.116567 | 31 | C |
| 2 | 0.470588 | 0.919598 | 0.524590 | 0.000000 | 0.000000 | 0.347243 | 0.253629 | 32 | B |
| 3 | 0.058824 | 0.447236 | 0.540984 | 0.232323 | 0.111111 | 0.418778 | 0.038002 | 21 | B |
| 4 | 0.000000 | 0.688442 | 0.327869 | 0.353535 | 0.198582 | 0.642325 | 0.943638 | 33 | C |
In [16]:
labels = diabetes['Class']
labels.head()
Out[16]:
0 1 1 0 2 1 3 0 4 1 Name: Class, dtype: int64
In [18]:
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X_data,labels,test_size=0.3,random_state=101)
In [19]:
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
(537, 9) (231, 9) (537,) (231,)
In [20]:
# Step 2 - Create Data Input Function - It acts like your Feed Dictionary,Batch Size
#We will pass same feature column name that we wrote in step 1
input_func = tf.estimator.inputs.pandas_input_fn(x=X_train,y=y_train,batch_size=10,num_epochs=1000,shuffle=True)
In [21]:
# Step 3 - Model Creation
model = tf.estimator.LinearClassifier(feature_columns = feat_cols,n_classes=2)
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: C:\Users\LUCKY_~1\AppData\Local\Temp\tmp0d73xtkj
INFO:tensorflow:Using config: {'_model_dir': 'C:\\Users\\LUCKY_~1\\AppData\\Local\\Temp\\tmp0d73xtkj', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x0000025EB1B5D7F0>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
In [22]:
# Step 4 - Model Training
model.train(input_fn=input_func,steps=1000)
WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\training\training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\inputs\queues\feeding_queue_runner.py:62: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the `tf.data` module. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\inputs\queues\feeding_functions.py:500: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the `tf.data` module. INFO:tensorflow:Calling model_fn. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\feature_column\feature_column_v2.py:305: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version. Instructions for updating: Please use `layer.add_weight` method instead. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version. Instructions for updating: If using Keras pass *_constraint arguments to layers. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\embedding_ops.py:802: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\linear.py:308: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\training\monitored_session.py:882: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the `tf.data` module. INFO:tensorflow:Saving checkpoints for 0 into C:\Users\LUCKY_~1\AppData\Local\Temp\tmp0d73xtkj\model.ckpt. INFO:tensorflow:loss = 6.931472, step = 0 INFO:tensorflow:global_step/sec: 16.131 INFO:tensorflow:loss = 6.731434, step = 100 (6.182 sec) INFO:tensorflow:global_step/sec: 57.0958 INFO:tensorflow:loss = 5.107793, step = 200 (1.751 sec) INFO:tensorflow:global_step/sec: 59.445 INFO:tensorflow:loss = 4.8017254, step = 300 (1.684 sec) INFO:tensorflow:global_step/sec: 52.5732 INFO:tensorflow:loss = 6.170915, step = 400 (1.898 sec) INFO:tensorflow:global_step/sec: 61.8383 INFO:tensorflow:loss = 4.541557, step = 500 (1.618 sec) INFO:tensorflow:global_step/sec: 64.8397 INFO:tensorflow:loss = 3.3417058, step = 600 (1.543 sec) INFO:tensorflow:global_step/sec: 57.347 INFO:tensorflow:loss = 5.242248, step = 700 (1.744 sec) INFO:tensorflow:global_step/sec: 71.8695 INFO:tensorflow:loss = 5.7507014, step = 800 (1.390 sec) INFO:tensorflow:global_step/sec: 77.9081 INFO:tensorflow:loss = 5.155717, step = 900 (1.284 sec) INFO:tensorflow:Saving checkpoints for 1000 into C:\Users\LUCKY_~1\AppData\Local\Temp\tmp0d73xtkj\model.ckpt. INFO:tensorflow:Loss for final step: 4.6098833.
Out[22]:
<tensorflow_estimator.python.estimator.canned.linear.LinearClassifier at 0x25eb2042cf8>
In [23]:
# Step 5 - Evaluate Model Trained
eval_input_func = tf.estimator.inputs.pandas_input_fn(x=X_test,y=y_test,
batch_size=10,
num_epochs=1,
shuffle=False) #To make sure we evaluate in same order - shuffle
In [24]:
results = model.evaluate(eval_input_func)
INFO:tensorflow:Calling model_fn. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\ops\metrics_impl.py:2026: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Deprecated in favor of operator or tf.math.divide. WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead. WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2020-08-20T11:21:57Z INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\LUCKY_~1\AppData\Local\Temp\tmp0d73xtkj\model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Finished evaluation at 2020-08-20-11:21:59 INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.74025977, accuracy_baseline = 0.64935064, auc = 0.7998766, auc_precision_recall = 0.64915895, average_loss = 0.5261694, global_step = 1000, label/mean = 0.35064936, loss = 5.0643806, precision = 0.6363636, prediction/mean = 0.38154146, recall = 0.60493827 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: C:\Users\LUCKY_~1\AppData\Local\Temp\tmp0d73xtkj\model.ckpt-1000
In [25]:
results
Out[25]:
{'accuracy': 0.74025977,
'accuracy_baseline': 0.64935064,
'auc': 0.7998766,
'auc_precision_recall': 0.64915895,
'average_loss': 0.5261694,
'label/mean': 0.35064936,
'loss': 5.0643806,
'precision': 0.6363636,
'prediction/mean': 0.38154146,
'recall': 0.60493827,
'global_step': 1000}
In [26]:
## Testing on New Data
## We are testing on our test set , you can pass your own
## We will not pass y , we will be predicting
pred_input_func = tf.estimator.inputs.pandas_input_fn(x=X_test,batch_size=10,num_epochs=1,shuffle=False)
predictions = model.predict(pred_input_func)
In [27]:
my_pred = list(predictions)
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\LUCKY_~1\AppData\Local\Temp\tmp0d73xtkj\model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op.
In [28]:
my_pred
Out[28]:
[{'logits': array([0.17799228], dtype=float32),
'logistic': array([0.54438096], dtype=float32),
'probabilities': array([0.45561904, 0.54438096], dtype=float32),
'class_ids': array([1], dtype=int64),
'classes': array([b'1'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([0.6846631], dtype=float32),
'logistic': array([0.66477865], dtype=float32),
'probabilities': array([0.33522132, 0.66477865], dtype=float32),
'class_ids': array([1], dtype=int64),
'classes': array([b'1'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-0.36630732], dtype=float32),
'logistic': array([0.4094336], dtype=float32),
'probabilities': array([0.5905664 , 0.40943357], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-0.69875723], dtype=float32),
'logistic': array([0.33208787], dtype=float32),
'probabilities': array([0.6679122, 0.3320878], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-1.7525241], dtype=float32),
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'probabilities': array([0.85227084, 0.1477291 ], dtype=float32),
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{'logits': array([0.7800819], dtype=float32),
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{'logits': array([0.5656329], dtype=float32),
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'classes': array([b'1'], dtype=object),
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{'logits': array([-0.55205745], dtype=float32),
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{'logits': array([-1.1411579], dtype=float32),
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{'logits': array([-1.0787947], dtype=float32),
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{'logits': array([-1.7766249], dtype=float32),
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{'logits': array([-1.6843524], dtype=float32),
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{'logits': array([1.7847784], dtype=float32),
'logistic': array([0.85628587], dtype=float32),
'probabilities': array([0.1437141 , 0.85628587], dtype=float32),
'class_ids': array([1], dtype=int64),
'classes': array([b'1'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([0.6734418], dtype=float32),
'logistic': array([0.6622734], dtype=float32),
'probabilities': array([0.3377266, 0.6622734], dtype=float32),
'class_ids': array([1], dtype=int64),
'classes': array([b'1'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-1.8676343], dtype=float32),
'logistic': array([0.13381569], dtype=float32),
'probabilities': array([0.86618435, 0.1338157 ], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([0.6042673], dtype=float32),
'logistic': array([0.64663196], dtype=float32),
'probabilities': array([0.353368 , 0.64663196], dtype=float32),
'class_ids': array([1], dtype=int64),
'classes': array([b'1'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)}]
Dense Neural Network Classifier¶
In [29]:
# Hidden_units - How many neurons you want and how many layers
#We want 3 layers with 10 neurons each
dnn_model = tf.estimator.DNNClassifier(hidden_units=[10,10,10],feature_columns=feat_cols,n_classes=2)
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: C:\Users\LUCKY_~1\AppData\Local\Temp\tmp27dms14z
INFO:tensorflow:Using config: {'_model_dir': 'C:\\Users\\LUCKY_~1\\AppData\\Local\\Temp\\tmp27dms14z', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x0000025ECE159278>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
In [30]:
# You will get error if you use previous input_func
dnn_model.train(input_fn=input_func,steps=1000)
INFO:tensorflow:Calling model_fn.
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-30-1088d91f0174> in <module> 1 # You will get error if you use previous input_func ----> 2 dnn_model.train(input_fn=input_func,steps=1000) ~\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners) 368 369 saving_listeners = _check_listeners_type(saving_listeners) --> 370 loss = self._train_model(input_fn, hooks, saving_listeners) 371 logging.info('Loss for final step: %s.', loss) 372 return self ~\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py in _train_model(self, input_fn, hooks, saving_listeners) 1159 return self._train_model_distributed(input_fn, hooks, saving_listeners) 1160 else: -> 1161 return self._train_model_default(input_fn, hooks, saving_listeners) 1162 1163 def _train_model_default(self, input_fn, hooks, saving_listeners): ~\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners) 1189 worker_hooks.extend(input_hooks) 1190 estimator_spec = self._call_model_fn( -> 1191 features, labels, ModeKeys.TRAIN, self.config) 1192 global_step_tensor = training_util.get_global_step(g) 1193 return self._train_with_estimator_spec(estimator_spec, worker_hooks, ~\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py in _call_model_fn(self, features, labels, mode, config) 1147 1148 logging.info('Calling model_fn.') -> 1149 model_fn_results = self._model_fn(features=features, **kwargs) 1150 logging.info('Done calling model_fn.') 1151 ~\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\dnn.py in _model_fn(features, labels, mode, config) 809 input_layer_partitioner=input_layer_partitioner, 810 config=config, --> 811 batch_norm=batch_norm) 812 813 super(DNNClassifier, self).__init__( ~\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\dnn.py in _dnn_model_fn(features, labels, mode, head, hidden_units, feature_columns, optimizer, activation_fn, dropout, input_layer_partitioner, config, use_tpu, batch_norm) 461 input_layer_partitioner=input_layer_partitioner, 462 batch_norm=batch_norm) --> 463 logits = logit_fn(features=features, mode=mode) 464 465 return _get_dnn_estimator_spec(use_tpu, head, features, labels, mode, ~\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\dnn.py in dnn_logit_fn(features, mode) 106 input_layer_partitioner, 107 batch_norm, --> 108 name='dnn') 109 return dnn_model(features, mode) 110 ~\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\dnn.py in __init__(self, units, hidden_units, feature_columns, activation_fn, dropout, input_layer_partitioner, batch_norm, name, **kwargs) 184 if feature_column_lib.is_feature_column_v2(feature_columns): 185 self._input_layer = dense_features.DenseFeatures( --> 186 feature_columns=feature_columns, name='input_layer') 187 else: 188 self._input_layer = feature_column.InputLayer( ~\Anaconda3\lib\site-packages\tensorflow_core\python\feature_column\dense_features.py in __init__(self, feature_columns, trainable, name, **kwargs) 84 name=name, 85 expected_column_type=fc.DenseColumn, ---> 86 **kwargs) 87 88 @property ~\Anaconda3\lib\site-packages\tensorflow_core\python\feature_column\feature_column_v2.py in __init__(self, feature_columns, expected_column_type, trainable, name, **kwargs) 378 'You can wrap a categorical column with an ' 379 'embedding_column or indicator_column. Given: {}'.format( --> 380 expected_column_type, column)) 381 382 def build(self, _): ValueError: Items of feature_columns must be a <class 'tensorflow.python.feature_column.feature_column_v2.DenseColumn'>. You can wrap a categorical column with an embedding_column or indicator_column. Given: VocabularyListCategoricalColumn(key='Group', vocabulary_list=('A', 'B', 'C', 'D'), dtype=tf.string, default_value=-1, num_oov_buckets=0)
In [31]:
# It gives error at Categorical Columns that we created
embedded_group_col = tf.feature_column.embedding_column(assigned_group,dimension=4)
In [32]:
# Reset Feat Columns
## Replace assigned_group with embedded_group_col
feat_cols = [num_preg,plasma_gluc,dias_press,tricep,insulin,bmi,diabetes_pedigree,embedded_group_col,age_bucket]
In [33]:
#Step 2 - Create input Function
input_func = tf.estimator.inputs.pandas_input_fn(X_train,y_train,batch_size=10,num_epochs=1000,shuffle=True)
In [34]:
# Step 3 - Create Model
dnn_model = tf.estimator.DNNClassifier(hidden_units=[10,10,10],feature_columns=feat_cols,n_classes=2)
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: C:\Users\LUCKY_~1\AppData\Local\Temp\tmpyjuq8kto
INFO:tensorflow:Using config: {'_model_dir': 'C:\\Users\\LUCKY_~1\\AppData\\Local\\Temp\\tmpyjuq8kto', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x0000025EB42264E0>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
In [35]:
# Step 4 - Train Model
dnn_model.train(input_fn=input_func,steps=1000)
INFO:tensorflow:Calling model_fn. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\feature_column\feature_column_v2.py:3079: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. Instructions for updating: The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead. WARNING:tensorflow:From C:\Users\Lucky_Rathod\Anaconda3\lib\site-packages\tensorflow_core\python\training\adagrad.py:76: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Saving checkpoints for 0 into C:\Users\LUCKY_~1\AppData\Local\Temp\tmpyjuq8kto\model.ckpt. INFO:tensorflow:loss = 7.425701, step = 0 INFO:tensorflow:global_step/sec: 26.4979 INFO:tensorflow:loss = 5.748277, step = 100 (3.778 sec) INFO:tensorflow:global_step/sec: 44.6717 INFO:tensorflow:loss = 4.7091885, step = 200 (2.238 sec) INFO:tensorflow:global_step/sec: 67.3143 INFO:tensorflow:loss = 4.3222723, step = 300 (1.484 sec) INFO:tensorflow:global_step/sec: 79.704 INFO:tensorflow:loss = 6.980344, step = 400 (1.254 sec) INFO:tensorflow:global_step/sec: 103.203 INFO:tensorflow:loss = 6.0570803, step = 500 (0.970 sec) INFO:tensorflow:global_step/sec: 70.7338 INFO:tensorflow:loss = 7.2208576, step = 600 (1.414 sec) INFO:tensorflow:global_step/sec: 76.2404 INFO:tensorflow:loss = 4.7417207, step = 700 (1.312 sec) INFO:tensorflow:global_step/sec: 74.6852 INFO:tensorflow:loss = 3.0607705, step = 800 (1.339 sec) INFO:tensorflow:global_step/sec: 92.9232 INFO:tensorflow:loss = 4.5430813, step = 900 (1.078 sec) INFO:tensorflow:Saving checkpoints for 1000 into C:\Users\LUCKY_~1\AppData\Local\Temp\tmpyjuq8kto\model.ckpt. INFO:tensorflow:Loss for final step: 5.5066185.
Out[35]:
<tensorflow_estimator.python.estimator.canned.dnn.DNNClassifier at 0x25eb42260f0>
In [36]:
# Step 5 - Eval Model
eval_input_func = tf.estimator.inputs.pandas_input_fn(x=X_test,y=y_test,batch_size=10,num_epochs=1,shuffle=False)
dnn_model.evaluate(eval_input_func)
INFO:tensorflow:Calling model_fn. WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead. WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2020-08-20T11:54:15Z INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\LUCKY_~1\AppData\Local\Temp\tmpyjuq8kto\model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Finished evaluation at 2020-08-20-11:54:17 INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.7619048, accuracy_baseline = 0.64935064, auc = 0.81189305, auc_precision_recall = 0.652876, average_loss = 0.5040666, global_step = 1000, label/mean = 0.35064936, loss = 4.8516407, precision = 0.7096774, prediction/mean = 0.32602334, recall = 0.54320985 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: C:\Users\LUCKY_~1\AppData\Local\Temp\tmpyjuq8kto\model.ckpt-1000
Out[36]:
{'accuracy': 0.7619048,
'accuracy_baseline': 0.64935064,
'auc': 0.81189305,
'auc_precision_recall': 0.652876,
'average_loss': 0.5040666,
'label/mean': 0.35064936,
'loss': 4.8516407,
'precision': 0.7096774,
'prediction/mean': 0.32602334,
'recall': 0.54320985,
'global_step': 1000}
Tensorflow Regression Excercise¶
California Housing Data
This data set contains information about all the block groups in California from the 1990 Census. In this sample a block group on average includes 1425.5 individuals living in a geographically compact area.
The task is to aproximate the median house value of each block from the values of the rest of the variables.
It has been obtained from the LIACC repository. The original page where the data set can be found is: http://www.liaad.up.pt/~ltorgo/Regression/DataSets.html.
The Features:
- housingMedianAge: continuous.
- totalRooms: continuous.
- totalBedrooms: continuous.
- population: continuous.
- households: continuous.
- medianIncome: continuous.
- medianHouseValue: continuous.
In [83]:
# Import Dataset
import pandas as pd
housing = pd.read_csv(r'C:\Users\Lucky_Rathod\Desktop\Udemy Courses\Complete Tensorflow\Original Notes\Tensorflow-Bootcamp-master\02-TensorFlow-Basics\cal_housing_clean.csv')
housing.head()
Out[83]:
| housingMedianAge | totalRooms | totalBedrooms | population | households | medianIncome | medianHouseValue | |
|---|---|---|---|---|---|---|---|
| 0 | 41.0 | 880.0 | 129.0 | 322.0 | 126.0 | 8.3252 | 452600.0 |
| 1 | 21.0 | 7099.0 | 1106.0 | 2401.0 | 1138.0 | 8.3014 | 358500.0 |
| 2 | 52.0 | 1467.0 | 190.0 | 496.0 | 177.0 | 7.2574 | 352100.0 |
| 3 | 52.0 | 1274.0 | 235.0 | 558.0 | 219.0 | 5.6431 | 341300.0 |
| 4 | 52.0 | 1627.0 | 280.0 | 565.0 | 259.0 | 3.8462 | 342200.0 |
In [84]:
#Describe
housing.describe().transpose()
Out[84]:
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| housingMedianAge | 20640.0 | 28.639486 | 12.585558 | 1.0000 | 18.0000 | 29.0000 | 37.00000 | 52.0000 |
| totalRooms | 20640.0 | 2635.763081 | 2181.615252 | 2.0000 | 1447.7500 | 2127.0000 | 3148.00000 | 39320.0000 |
| totalBedrooms | 20640.0 | 537.898014 | 421.247906 | 1.0000 | 295.0000 | 435.0000 | 647.00000 | 6445.0000 |
| population | 20640.0 | 1425.476744 | 1132.462122 | 3.0000 | 787.0000 | 1166.0000 | 1725.00000 | 35682.0000 |
| households | 20640.0 | 499.539680 | 382.329753 | 1.0000 | 280.0000 | 409.0000 | 605.00000 | 6082.0000 |
| medianIncome | 20640.0 | 3.870671 | 1.899822 | 0.4999 | 2.5634 | 3.5348 | 4.74325 | 15.0001 |
| medianHouseValue | 20640.0 | 206855.816909 | 115395.615874 | 14999.0000 | 119600.0000 | 179700.0000 | 264725.00000 | 500001.0000 |
In [85]:
y_val = housing['medianHouseValue']
y_val.head()
Out[85]:
0 452600.0 1 358500.0 2 352100.0 3 341300.0 4 342200.0 Name: medianHouseValue, dtype: float64
In [86]:
x_data = housing.drop('medianHouseValue',axis=1)
x_data.head()
Out[86]:
| housingMedianAge | totalRooms | totalBedrooms | population | households | medianIncome | |
|---|---|---|---|---|---|---|
| 0 | 41.0 | 880.0 | 129.0 | 322.0 | 126.0 | 8.3252 |
| 1 | 21.0 | 7099.0 | 1106.0 | 2401.0 | 1138.0 | 8.3014 |
| 2 | 52.0 | 1467.0 | 190.0 | 496.0 | 177.0 | 7.2574 |
| 3 | 52.0 | 1274.0 | 235.0 | 558.0 | 219.0 | 5.6431 |
| 4 | 52.0 | 1627.0 | 280.0 | 565.0 | 259.0 | 3.8462 |
In [87]:
# Train Test Split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x_data, y_val, test_size=0.30, random_state=101)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
(14448, 6) (6192, 6) (14448,) (6192,)
Scale the Feature Data¶
Use sklearn preprocessing to create a MinMaxScaler for the feature data. Fit this scaler only to the training data. Then use it to transform X_test and X_train. Then use the scaled X_test and X_train along with pd.Dataframe to re-create two dataframes of scaled data.
In [88]:
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(X_train)
Out[88]:
MinMaxScaler(copy=True, feature_range=(0, 1))
In [89]:
X_train = pd.DataFrame(data = scaler.transform(X_train),
columns = X_train.columns,
index = X_train.index)
X_train.head()
Out[89]:
| housingMedianAge | totalRooms | totalBedrooms | population | households | medianIncome | |
|---|---|---|---|---|---|---|
| 6761 | 0.352941 | 0.069688 | 0.117163 | 0.048769 | 0.115442 | 0.142508 |
| 3010 | 0.607843 | 0.011242 | 0.015673 | 0.008367 | 0.014142 | 0.045027 |
| 7812 | 0.666667 | 0.025230 | 0.031347 | 0.020971 | 0.030258 | 0.212866 |
| 8480 | 0.666667 | 0.032530 | 0.033830 | 0.024752 | 0.030094 | 0.298651 |
| 1051 | 0.294118 | 0.031919 | 0.035692 | 0.019466 | 0.034863 | 0.272631 |
In [90]:
X_test = pd.DataFrame(data = scaler.transform(X_test),
columns = X_test.columns,
index = X_test.index)
X_test.head()
Out[90]:
| housingMedianAge | totalRooms | totalBedrooms | population | households | medianIncome | |
|---|---|---|---|---|---|---|
| 16086 | 0.686275 | 0.046264 | 0.045158 | 0.025873 | 0.048841 | 0.353133 |
| 8816 | 0.705882 | 0.027417 | 0.020795 | 0.012709 | 0.023187 | 0.770182 |
| 7175 | 0.901961 | 0.032326 | 0.040813 | 0.041662 | 0.042592 | 0.133626 |
| 16714 | 0.313725 | 0.043212 | 0.046089 | 0.032840 | 0.048018 | 0.263576 |
| 14491 | 0.411765 | 0.088433 | 0.069367 | 0.043728 | 0.072192 | 0.660046 |
In [91]:
y_test.head()
Out[91]:
16086 406200.0 8816 500001.0 7175 122900.0 16714 209100.0 14491 500001.0 Name: medianHouseValue, dtype: float64
In [92]:
housing.columns
Out[92]:
Index(['housingMedianAge', 'totalRooms', 'totalBedrooms', 'population',
'households', 'medianIncome', 'medianHouseValue'],
dtype='object')
In [93]:
import tensorflow as tf
In [94]:
# Step 1 - Create List of Feature Columns
## Numeric
age = tf.feature_column.numeric_column('housingMedianAge')
rooms = tf.feature_column.numeric_column('totalRooms')
bedrooms = tf.feature_column.numeric_column('totalBedrooms')
pop = tf.feature_column.numeric_column('population')
households = tf.feature_column.numeric_column('households')
income = tf.feature_column.numeric_column('medianIncome')
feat_cols = [ age,rooms,bedrooms,pop,households,income]
In [95]:
y_train.head()
Out[95]:
6761 197500.0 3010 150000.0 7812 178000.0 8480 152800.0 1051 144300.0 Name: medianHouseValue, dtype: float64
In [96]:
# Step 2 - Create Input Functions - Training
input_func = tf.estimator.inputs.pandas_input_fn(x=X_train,y=y_train ,batch_size=10,num_epochs=1000,
shuffle=True)
In [97]:
# Step 3 - Create Estimator Model
model = tf.estimator.DNNRegressor(hidden_units=[6,6,6],feature_columns=feat_cols)
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: C:\Users\LUCKY_~1\AppData\Local\Temp\tmpu3v2b8rd
INFO:tensorflow:Using config: {'_model_dir': 'C:\\Users\\LUCKY_~1\\AppData\\Local\\Temp\\tmpu3v2b8rd', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x0000025F9B51FB38>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
In [98]:
# Step 4 - Train Model with input_func created in step 2
model.train(input_fn=input_func,steps=20000)
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Saving checkpoints for 0 into C:\Users\LUCKY_~1\AppData\Local\Temp\tmpu3v2b8rd\model.ckpt. INFO:tensorflow:loss = 359474720000.0, step = 0 INFO:tensorflow:global_step/sec: 57.7246 INFO:tensorflow:loss = 438669570000.0, step = 100 (1.731 sec) INFO:tensorflow:global_step/sec: 109.343 INFO:tensorflow:loss = 546009970000.0, step = 200 (0.915 sec) INFO:tensorflow:global_step/sec: 125.335 INFO:tensorflow:loss = 852174900000.0, step = 300 (0.797 sec) INFO:tensorflow:global_step/sec: 116.05 INFO:tensorflow:loss = 719156800000.0, step = 400 (0.863 sec) INFO:tensorflow:global_step/sec: 144.895 INFO:tensorflow:loss = 741133250000.0, step = 500 (0.688 sec) INFO:tensorflow:global_step/sec: 147.669 INFO:tensorflow:loss = 704583760000.0, step = 600 (0.678 sec) INFO:tensorflow:global_step/sec: 134.587 INFO:tensorflow:loss = 256341930000.0, step = 700 (0.744 sec) INFO:tensorflow:global_step/sec: 131.24 INFO:tensorflow:loss = 404430000000.0, step = 800 (0.761 sec) INFO:tensorflow:global_step/sec: 131.24 INFO:tensorflow:loss = 387116500000.0, step = 900 (0.763 sec) INFO:tensorflow:global_step/sec: 144.063 INFO:tensorflow:loss = 149909550000.0, step = 1000 (0.695 sec) INFO:tensorflow:global_step/sec: 108.572 INFO:tensorflow:loss = 565779600000.0, step = 1100 (0.921 sec) INFO:tensorflow:global_step/sec: 126.123 INFO:tensorflow:loss = 144369120000.0, step = 1200 (0.791 sec) INFO:tensorflow:global_step/sec: 148.544 INFO:tensorflow:loss = 219415130000.0, step = 1300 (0.673 sec) INFO:tensorflow:global_step/sec: 94.1876 INFO:tensorflow:loss = 269963760000.0, step = 1400 (1.064 sec) INFO:tensorflow:global_step/sec: 108.515 INFO:tensorflow:loss = 375071700000.0, step = 1500 (0.920 sec) INFO:tensorflow:global_step/sec: 86.2145 INFO:tensorflow:loss = 186928250000.0, step = 1600 (1.163 sec) INFO:tensorflow:global_step/sec: 121.306 INFO:tensorflow:loss = 147064650000.0, step = 1700 (0.821 sec) INFO:tensorflow:global_step/sec: 104.33 INFO:tensorflow:loss = 96777800000.0, step = 1800 (0.965 sec) INFO:tensorflow:global_step/sec: 101.89 INFO:tensorflow:loss = 195997070000.0, step = 1900 (0.975 sec) INFO:tensorflow:global_step/sec: 126.26 INFO:tensorflow:loss = 37259140000.0, step = 2000 (0.793 sec) INFO:tensorflow:global_step/sec: 114.787 INFO:tensorflow:loss = 139710760000.0, step = 2100 (0.870 sec) INFO:tensorflow:global_step/sec: 145.702 INFO:tensorflow:loss = 45823156000.0, step = 2200 (0.686 sec) INFO:tensorflow:global_step/sec: 127.484 INFO:tensorflow:loss = 192011700000.0, step = 2300 (0.786 sec) INFO:tensorflow:global_step/sec: 151.026 INFO:tensorflow:loss = 50523423000.0, step = 2400 (0.659 sec) INFO:tensorflow:global_step/sec: 161.983 INFO:tensorflow:loss = 217915920000.0, step = 2500 (0.618 sec) INFO:tensorflow:global_step/sec: 103.199 INFO:tensorflow:loss = 109238624000.0, step = 2600 (0.970 sec) INFO:tensorflow:global_step/sec: 126.734 INFO:tensorflow:loss = 28744397000.0, step = 2700 (0.788 sec) INFO:tensorflow:global_step/sec: 104.498 INFO:tensorflow:loss = 174847360000.0, step = 2800 (0.959 sec) INFO:tensorflow:global_step/sec: 113.883 INFO:tensorflow:loss = 159783930000.0, step = 2900 (0.876 sec) INFO:tensorflow:global_step/sec: 107.756 INFO:tensorflow:loss = 181496090000.0, step = 3000 (0.929 sec) INFO:tensorflow:global_step/sec: 108.013 INFO:tensorflow:loss = 130975010000.0, step = 3100 (0.925 sec) INFO:tensorflow:global_step/sec: 146.162 INFO:tensorflow:loss = 73867805000.0, step = 3200 (0.688 sec) INFO:tensorflow:global_step/sec: 74.7708 INFO:tensorflow:loss = 109740425000.0, step = 3300 (1.335 sec) INFO:tensorflow:global_step/sec: 116.862 INFO:tensorflow:loss = 107676940000.0, step = 3400 (0.855 sec) WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 3431 vs previous value: 3431. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize. INFO:tensorflow:global_step/sec: 77.4865 INFO:tensorflow:loss = 110180440000.0, step = 3500 (1.290 sec) INFO:tensorflow:global_step/sec: 108.397 INFO:tensorflow:loss = 253282980000.0, step = 3600 (0.927 sec) INFO:tensorflow:global_step/sec: 107.696 INFO:tensorflow:loss = 44873028000.0, step = 3700 (0.925 sec) INFO:tensorflow:global_step/sec: 147.452 INFO:tensorflow:loss = 123014220000.0, step = 3800 (0.677 sec) INFO:tensorflow:global_step/sec: 130.049 INFO:tensorflow:loss = 90315610000.0, step = 3900 (0.770 sec) INFO:tensorflow:global_step/sec: 161.202 INFO:tensorflow:loss = 99819730000.0, step = 4000 (0.619 sec) INFO:tensorflow:global_step/sec: 184.655 INFO:tensorflow:loss = 58098600000.0, step = 4100 (0.543 sec) INFO:tensorflow:global_step/sec: 141.222 INFO:tensorflow:loss = 85404470000.0, step = 4200 (0.709 sec) INFO:tensorflow:global_step/sec: 146.726 INFO:tensorflow:loss = 327374000000.0, step = 4300 (0.681 sec) INFO:tensorflow:global_step/sec: 182.637 INFO:tensorflow:loss = 127717655000.0, step = 4400 (0.547 sec) INFO:tensorflow:global_step/sec: 141.222 INFO:tensorflow:loss = 86888790000.0, step = 4500 (0.710 sec) INFO:tensorflow:global_step/sec: 123.634 INFO:tensorflow:loss = 289227080000.0, step = 4600 (0.807 sec) INFO:tensorflow:global_step/sec: 125.649 INFO:tensorflow:loss = 27015254000.0, step = 4700 (0.800 sec) INFO:tensorflow:global_step/sec: 101.178 INFO:tensorflow:loss = 51258810000.0, step = 4800 (0.985 sec) INFO:tensorflow:global_step/sec: 131.758 INFO:tensorflow:loss = 172040180000.0, step = 4900 (0.760 sec) INFO:tensorflow:global_step/sec: 98.6885 INFO:tensorflow:loss = 133794950000.0, step = 5000 (1.014 sec) INFO:tensorflow:global_step/sec: 95.7666 INFO:tensorflow:loss = 128489180000.0, step = 5100 (1.045 sec) INFO:tensorflow:global_step/sec: 79.3562 INFO:tensorflow:loss = 138006540000.0, step = 5200 (1.259 sec) INFO:tensorflow:global_step/sec: 151.462 INFO:tensorflow:loss = 199591530000.0, step = 5300 (0.669 sec) INFO:tensorflow:global_step/sec: 87.954 INFO:tensorflow:loss = 87019650000.0, step = 5400 (1.127 sec) INFO:tensorflow:global_step/sec: 127.243 INFO:tensorflow:loss = 108367670000.0, step = 5500 (0.785 sec) INFO:tensorflow:global_step/sec: 145.207 INFO:tensorflow:loss = 46691594000.0, step = 5600 (0.690 sec) INFO:tensorflow:global_step/sec: 131.069 INFO:tensorflow:loss = 141144500000.0, step = 5700 (0.761 sec) INFO:tensorflow:global_step/sec: 185.338 INFO:tensorflow:loss = 68776510000.0, step = 5800 (0.540 sec) INFO:tensorflow:global_step/sec: 169.657 INFO:tensorflow:loss = 35566375000.0, step = 5900 (0.592 sec) INFO:tensorflow:global_step/sec: 125.649 INFO:tensorflow:loss = 150032830000.0, step = 6000 (0.794 sec) INFO:tensorflow:global_step/sec: 184.654 INFO:tensorflow:loss = 65325980000.0, step = 6100 (0.541 sec) INFO:tensorflow:global_step/sec: 180.337 INFO:tensorflow:loss = 57843590000.0, step = 6200 (0.556 sec) INFO:tensorflow:global_step/sec: 135.132 INFO:tensorflow:loss = 120516680000.0, step = 6300 (0.740 sec) INFO:tensorflow:global_step/sec: 179.37 INFO:tensorflow:loss = 133490380000.0, step = 6400 (0.558 sec) INFO:tensorflow:global_step/sec: 167.952 INFO:tensorflow:loss = 69490295000.0, step = 6500 (0.596 sec) INFO:tensorflow:global_step/sec: 100.821 INFO:tensorflow:loss = 114635014000.0, step = 6600 (0.991 sec) INFO:tensorflow:global_step/sec: 132.105 INFO:tensorflow:loss = 86504410000.0, step = 6700 (0.758 sec) INFO:tensorflow:global_step/sec: 125.022 INFO:tensorflow:loss = 61539717000.0, step = 6800 (0.803 sec) INFO:tensorflow:global_step/sec: 106.103 INFO:tensorflow:loss = 98104310000.0, step = 6900 (0.938 sec) INFO:tensorflow:global_step/sec: 144.896 INFO:tensorflow:loss = 112963120000.0, step = 7000 (0.689 sec) INFO:tensorflow:global_step/sec: 131.069 INFO:tensorflow:loss = 158021030000.0, step = 7100 (0.764 sec) INFO:tensorflow:global_step/sec: 85.6987 INFO:tensorflow:loss = 205129740000.0, step = 7200 (1.170 sec) INFO:tensorflow:global_step/sec: 119.224 INFO:tensorflow:loss = 90334920000.0, step = 7300 (0.835 sec) INFO:tensorflow:global_step/sec: 116.455 INFO:tensorflow:loss = 191614570000.0, step = 7400 (0.862 sec) WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 7401 vs previous value: 7401. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize. INFO:tensorflow:global_step/sec: 85.6255 INFO:tensorflow:loss = 173913540000.0, step = 7500 (1.167 sec) INFO:tensorflow:global_step/sec: 105.434 INFO:tensorflow:loss = 194989590000.0, step = 7600 (0.947 sec) INFO:tensorflow:global_step/sec: 111.781 INFO:tensorflow:loss = 60459020000.0, step = 7700 (0.895 sec) INFO:tensorflow:global_step/sec: 114.723 INFO:tensorflow:loss = 104390980000.0, step = 7800 (0.874 sec) INFO:tensorflow:global_step/sec: 113.041 INFO:tensorflow:loss = 63079970000.0, step = 7900 (0.883 sec) INFO:tensorflow:global_step/sec: 142.425 INFO:tensorflow:loss = 178233640000.0, step = 8000 (0.702 sec) INFO:tensorflow:global_step/sec: 115.649 INFO:tensorflow:loss = 119045775000.0, step = 8100 (0.867 sec) INFO:tensorflow:global_step/sec: 111.532 INFO:tensorflow:loss = 41686720000.0, step = 8200 (0.896 sec) INFO:tensorflow:global_step/sec: 165.732 INFO:tensorflow:loss = 115057210000.0, step = 8300 (0.602 sec) INFO:tensorflow:global_step/sec: 101.588 INFO:tensorflow:loss = 121128714000.0, step = 8400 (0.986 sec) WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 8401 vs previous value: 8401. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize. INFO:tensorflow:global_step/sec: 115.383 INFO:tensorflow:loss = 70263000000.0, step = 8500 (0.869 sec) INFO:tensorflow:global_step/sec: 97.5365 INFO:tensorflow:loss = 41518535000.0, step = 8600 (1.023 sec) INFO:tensorflow:global_step/sec: 108.394 INFO:tensorflow:loss = 53327980000.0, step = 8700 (0.923 sec) INFO:tensorflow:global_step/sec: 106.216 INFO:tensorflow:loss = 153982200000.0, step = 8800 (0.939 sec) INFO:tensorflow:global_step/sec: 123.179 INFO:tensorflow:loss = 68247335000.0, step = 8900 (0.812 sec) INFO:tensorflow:global_step/sec: 91.3183 INFO:tensorflow:loss = 57739370000.0, step = 9000 (1.096 sec) INFO:tensorflow:global_step/sec: 84.0462 INFO:tensorflow:loss = 67537280000.0, step = 9100 (1.198 sec) INFO:tensorflow:global_step/sec: 79.2617 INFO:tensorflow:loss = 137434464000.0, step = 9200 (1.254 sec) INFO:tensorflow:global_step/sec: 88.8109 INFO:tensorflow:loss = 202658000000.0, step = 9300 (1.127 sec) INFO:tensorflow:global_step/sec: 96.5039 INFO:tensorflow:loss = 86435635000.0, step = 9400 (1.035 sec) INFO:tensorflow:global_step/sec: 116.455 INFO:tensorflow:loss = 57150505000.0, step = 9500 (0.858 sec) INFO:tensorflow:global_step/sec: 118.519 INFO:tensorflow:loss = 125204070000.0, step = 9600 (0.844 sec) INFO:tensorflow:global_step/sec: 112.66 INFO:tensorflow:loss = 80896370000.0, step = 9700 (0.888 sec) INFO:tensorflow:global_step/sec: 111.285 INFO:tensorflow:loss = 95130450000.0, step = 9800 (0.902 sec) INFO:tensorflow:global_step/sec: 100.067 INFO:tensorflow:loss = 110504360000.0, step = 9900 (0.996 sec) INFO:tensorflow:global_step/sec: 134.048 INFO:tensorflow:loss = 96758510000.0, step = 10000 (0.746 sec) INFO:tensorflow:global_step/sec: 119.082 INFO:tensorflow:loss = 214870820000.0, step = 10100 (0.840 sec) INFO:tensorflow:global_step/sec: 115.649 INFO:tensorflow:loss = 89198854000.0, step = 10200 (0.865 sec) INFO:tensorflow:global_step/sec: 135.131 INFO:tensorflow:loss = 154599030000.0, step = 10300 (0.742 sec) INFO:tensorflow:global_step/sec: 98.9809 INFO:tensorflow:loss = 65636655000.0, step = 10400 (1.008 sec) INFO:tensorflow:global_step/sec: 165.458 INFO:tensorflow:loss = 67618160000.0, step = 10500 (0.604 sec) INFO:tensorflow:global_step/sec: 148.105 INFO:tensorflow:loss = 196420700000.0, step = 10600 (0.675 sec) INFO:tensorflow:global_step/sec: 106.895 INFO:tensorflow:loss = 32708729000.0, step = 10700 (0.935 sec) INFO:tensorflow:global_step/sec: 158.651 INFO:tensorflow:loss = 104970990000.0, step = 10800 (0.630 sec) INFO:tensorflow:global_step/sec: 92.9264 INFO:tensorflow:loss = 36357000000.0, step = 10900 (1.078 sec) INFO:tensorflow:global_step/sec: 119.366 INFO:tensorflow:loss = 65157005000.0, step = 11000 (0.836 sec) INFO:tensorflow:global_step/sec: 107.238 INFO:tensorflow:loss = 49949647000.0, step = 11100 (0.933 sec) INFO:tensorflow:global_step/sec: 126.359 INFO:tensorflow:loss = 76077300000.0, step = 11200 (0.791 sec) INFO:tensorflow:global_step/sec: 143.444 INFO:tensorflow:loss = 85345200000.0, step = 11300 (0.699 sec) INFO:tensorflow:global_step/sec: 105.991 INFO:tensorflow:loss = 116414110000.0, step = 11400 (0.941 sec) INFO:tensorflow:global_step/sec: 167.113 INFO:tensorflow:loss = 117577410000.0, step = 11500 (0.597 sec) INFO:tensorflow:global_step/sec: 174.075 INFO:tensorflow:loss = 77000970000.0, step = 11600 (0.575 sec) INFO:tensorflow:global_step/sec: 115.118 INFO:tensorflow:loss = 146089510000.0, step = 11700 (0.870 sec) INFO:tensorflow:global_step/sec: 114.592 INFO:tensorflow:loss = 160629690000.0, step = 11800 (0.871 sec) INFO:tensorflow:global_step/sec: 111.037 INFO:tensorflow:loss = 126299590000.0, step = 11900 (0.902 sec) INFO:tensorflow:global_step/sec: 136.42 INFO:tensorflow:loss = 59895267000.0, step = 12000 (0.733 sec) INFO:tensorflow:global_step/sec: 179.369 INFO:tensorflow:loss = 82510930000.0, step = 12100 (0.558 sec) INFO:tensorflow:global_step/sec: 112.534 INFO:tensorflow:loss = 110811890000.0, step = 12200 (0.890 sec) INFO:tensorflow:global_step/sec: 134.95 INFO:tensorflow:loss = 77876080000.0, step = 12300 (0.797 sec) INFO:tensorflow:global_step/sec: 119.083 INFO:tensorflow:loss = 66619515000.0, step = 12400 (0.783 sec) INFO:tensorflow:global_step/sec: 117.449 INFO:tensorflow:loss = 211590320000.0, step = 12500 (0.852 sec) INFO:tensorflow:global_step/sec: 178.73 INFO:tensorflow:loss = 95833920000.0, step = 12600 (0.559 sec) INFO:tensorflow:global_step/sec: 139.454 INFO:tensorflow:loss = 59489874000.0, step = 12700 (0.718 sec) INFO:tensorflow:global_step/sec: 99.0788 INFO:tensorflow:loss = 55915850000.0, step = 12800 (1.012 sec) INFO:tensorflow:global_step/sec: 120.081 INFO:tensorflow:loss = 100405370000.0, step = 12900 (0.830 sec) INFO:tensorflow:global_step/sec: 118.8 INFO:tensorflow:loss = 132710570000.0, step = 13000 (0.842 sec) INFO:tensorflow:global_step/sec: 118.869 INFO:tensorflow:loss = 88307370000.0, step = 13100 (0.841 sec) INFO:tensorflow:global_step/sec: 139.067 INFO:tensorflow:loss = 51362374000.0, step = 13200 (0.719 sec) INFO:tensorflow:global_step/sec: 131.24 INFO:tensorflow:loss = 67748620000.0, step = 13300 (0.761 sec) INFO:tensorflow:global_step/sec: 182.305 INFO:tensorflow:loss = 94743710000.0, step = 13400 (0.548 sec) INFO:tensorflow:global_step/sec: 163.302 INFO:tensorflow:loss = 169497070000.0, step = 13500 (0.614 sec) INFO:tensorflow:global_step/sec: 131.24 INFO:tensorflow:loss = 139775340000.0, step = 13600 (0.761 sec) INFO:tensorflow:global_step/sec: 169.658 INFO:tensorflow:loss = 145638650000.0, step = 13700 (0.588 sec) INFO:tensorflow:global_step/sec: 173.473 INFO:tensorflow:loss = 52269048000.0, step = 13800 (0.578 sec) INFO:tensorflow:global_step/sec: 131.585 INFO:tensorflow:loss = 72951760000.0, step = 13900 (0.759 sec) INFO:tensorflow:global_step/sec: 158.651 INFO:tensorflow:loss = 62386524000.0, step = 14000 (0.630 sec) INFO:tensorflow:global_step/sec: 174.378 INFO:tensorflow:loss = 51261985000.0, step = 14100 (0.573 sec) INFO:tensorflow:global_step/sec: 132.454 INFO:tensorflow:loss = 297391950000.0, step = 14200 (0.755 sec) INFO:tensorflow:global_step/sec: 135.314 INFO:tensorflow:loss = 54169588000.0, step = 14300 (0.739 sec) INFO:tensorflow:global_step/sec: 161.201 INFO:tensorflow:loss = 144094820000.0, step = 14400 (0.620 sec) INFO:tensorflow:global_step/sec: 127.567 INFO:tensorflow:loss = 149637250000.0, step = 14500 (0.785 sec) INFO:tensorflow:global_step/sec: 164.914 INFO:tensorflow:loss = 99051180000.0, step = 14600 (0.604 sec) INFO:tensorflow:global_step/sec: 171.691 INFO:tensorflow:loss = 96519815000.0, step = 14700 (0.584 sec) INFO:tensorflow:global_step/sec: 108.515 INFO:tensorflow:loss = 184110970000.0, step = 14800 (0.921 sec) INFO:tensorflow:global_step/sec: 125.649 INFO:tensorflow:loss = 170490120000.0, step = 14900 (0.796 sec) INFO:tensorflow:global_step/sec: 99.6696 INFO:tensorflow:loss = 135387510000.0, step = 15000 (1.004 sec) INFO:tensorflow:global_step/sec: 119.651 INFO:tensorflow:loss = 71078720000.0, step = 15100 (0.835 sec) INFO:tensorflow:global_step/sec: 94.6814 INFO:tensorflow:loss = 112967385000.0, step = 15200 (1.058 sec) INFO:tensorflow:global_step/sec: 125.964 INFO:tensorflow:loss = 129326610000.0, step = 15300 (0.793 sec) INFO:tensorflow:global_step/sec: 104.664 INFO:tensorflow:loss = 83986830000.0, step = 15400 (0.954 sec) INFO:tensorflow:global_step/sec: 131.412 INFO:tensorflow:loss = 55383660000.0, step = 15500 (0.761 sec) INFO:tensorflow:global_step/sec: 119.937 INFO:tensorflow:loss = 176951260000.0, step = 15600 (0.835 sec) INFO:tensorflow:global_step/sec: 129.044 INFO:tensorflow:loss = 121042340000.0, step = 15700 (0.775 sec) INFO:tensorflow:global_step/sec: 130.38 INFO:tensorflow:loss = 176786590000.0, step = 15800 (0.767 sec) INFO:tensorflow:global_step/sec: 105.433 INFO:tensorflow:loss = 107802530000.0, step = 15900 (0.947 sec) INFO:tensorflow:global_step/sec: 141.222 INFO:tensorflow:loss = 67546700000.0, step = 16000 (0.708 sec) INFO:tensorflow:global_step/sec: 125.649 INFO:tensorflow:loss = 122525500000.0, step = 16100 (0.797 sec) INFO:tensorflow:global_step/sec: 127.082 INFO:tensorflow:loss = 72838040000.0, step = 16200 (0.786 sec) INFO:tensorflow:global_step/sec: 145.316 INFO:tensorflow:loss = 113358090000.0, step = 16300 (0.689 sec) INFO:tensorflow:global_step/sec: 112.787 INFO:tensorflow:loss = 37269172000.0, step = 16400 (0.887 sec) INFO:tensorflow:global_step/sec: 117.823 INFO:tensorflow:loss = 75292360000.0, step = 16500 (0.848 sec) INFO:tensorflow:global_step/sec: 120.946 INFO:tensorflow:loss = 62889920000.0, step = 16600 (0.828 sec) INFO:tensorflow:global_step/sec: 130.387 INFO:tensorflow:loss = 105358030000.0, step = 16700 (0.765 sec) INFO:tensorflow:global_step/sec: 174.378 INFO:tensorflow:loss = 173334720000.0, step = 16800 (0.574 sec) INFO:tensorflow:global_step/sec: 146.376 INFO:tensorflow:loss = 36176986000.0, step = 16900 (0.685 sec) INFO:tensorflow:global_step/sec: 123.634 INFO:tensorflow:loss = 148963670000.0, step = 17000 (0.808 sec) INFO:tensorflow:global_step/sec: 156.668 INFO:tensorflow:loss = 171715330000.0, step = 17100 (0.639 sec) INFO:tensorflow:global_step/sec: 106.667 INFO:tensorflow:loss = 90684440000.0, step = 17200 (0.935 sec) INFO:tensorflow:global_step/sec: 119.938 INFO:tensorflow:loss = 75250760000.0, step = 17300 (0.834 sec) INFO:tensorflow:global_step/sec: 143.436 INFO:tensorflow:loss = 28742513000.0, step = 17400 (0.699 sec) INFO:tensorflow:global_step/sec: 122.877 INFO:tensorflow:loss = 200500630000.0, step = 17500 (0.813 sec) INFO:tensorflow:global_step/sec: 141.421 INFO:tensorflow:loss = 140520930000.0, step = 17600 (0.705 sec) INFO:tensorflow:global_step/sec: 148.106 INFO:tensorflow:loss = 191274220000.0, step = 17700 (0.678 sec) INFO:tensorflow:global_step/sec: 122.278 INFO:tensorflow:loss = 47254400000.0, step = 17800 (0.816 sec) INFO:tensorflow:global_step/sec: 174.987 INFO:tensorflow:loss = 120707990000.0, step = 17900 (0.571 sec) INFO:tensorflow:global_step/sec: 168.517 INFO:tensorflow:loss = 191569790000.0, step = 18000 (0.594 sec) INFO:tensorflow:global_step/sec: 112.152 INFO:tensorflow:loss = 123306530000.0, step = 18100 (0.891 sec) INFO:tensorflow:global_step/sec: 134.407 INFO:tensorflow:loss = 100582195000.0, step = 18200 (0.744 sec) INFO:tensorflow:global_step/sec: 110.063 INFO:tensorflow:loss = 101575280000.0, step = 18300 (0.909 sec) INFO:tensorflow:global_step/sec: 147.236 INFO:tensorflow:loss = 101342626000.0, step = 18400 (0.679 sec) INFO:tensorflow:global_step/sec: 131.931 INFO:tensorflow:loss = 24113803000.0, step = 18500 (0.758 sec) INFO:tensorflow:global_step/sec: 166.282 INFO:tensorflow:loss = 148454540000.0, step = 18600 (0.601 sec) INFO:tensorflow:global_step/sec: 160.171 INFO:tensorflow:loss = 50637873000.0, step = 18700 (0.625 sec) INFO:tensorflow:global_step/sec: 128.548 INFO:tensorflow:loss = 117659880000.0, step = 18800 (0.777 sec) INFO:tensorflow:global_step/sec: 170.234 INFO:tensorflow:loss = 175942030000.0, step = 18900 (0.587 sec) INFO:tensorflow:global_step/sec: 164.372 INFO:tensorflow:loss = 89169870000.0, step = 19000 (0.609 sec) INFO:tensorflow:global_step/sec: 111.162 INFO:tensorflow:loss = 134248020000.0, step = 19100 (0.901 sec) INFO:tensorflow:global_step/sec: 157.653 INFO:tensorflow:loss = 77520675000.0, step = 19200 (0.631 sec) INFO:tensorflow:global_step/sec: 136.791 INFO:tensorflow:loss = 61806166000.0, step = 19300 (0.733 sec) INFO:tensorflow:global_step/sec: 122.577 INFO:tensorflow:loss = 61596570000.0, step = 19400 (0.815 sec) INFO:tensorflow:global_step/sec: 171.985 INFO:tensorflow:loss = 54084720000.0, step = 19500 (0.581 sec) INFO:tensorflow:global_step/sec: 146.376 INFO:tensorflow:loss = 86012820000.0, step = 19600 (0.683 sec) INFO:tensorflow:global_step/sec: 136.418 INFO:tensorflow:loss = 176772020000.0, step = 19700 (0.732 sec) INFO:tensorflow:global_step/sec: 167.671 INFO:tensorflow:loss = 126563000000.0, step = 19800 (0.598 sec) INFO:tensorflow:global_step/sec: 98.6882 INFO:tensorflow:loss = 201465120000.0, step = 19900 (1.014 sec) INFO:tensorflow:Saving checkpoints for 20000 into C:\Users\LUCKY_~1\AppData\Local\Temp\tmpu3v2b8rd\model.ckpt. INFO:tensorflow:Loss for final step: 79876060000.0.
Out[98]:
<tensorflow_estimator.python.estimator.canned.dnn.DNNRegressor at 0x25f9afec240>
In [100]:
# Step 5 - Evaluate
eval_input_func = tf.estimator.inputs.pandas_input_fn(x = X_test,y=y_test,batch_size=10,num_epochs=1,shuffle=False)
evalmetrics = model.evaluate(input_fn=eval_input_func)
evalmetrics
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2020-08-20T16:11:45Z INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\LUCKY_~1\AppData\Local\Temp\tmpu3v2b8rd\model.ckpt-20000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Finished evaluation at 2020-08-20-16:11:51 INFO:tensorflow:Saving dict for global step 20000: average_loss = 10137248000.0, global_step = 20000, label/mean = 207662.89, loss = 101241676000.0, prediction/mean = 211164.14 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20000: C:\Users\LUCKY_~1\AppData\Local\Temp\tmpu3v2b8rd\model.ckpt-20000
Out[100]:
{'average_loss': 10137248000.0,
'label/mean': 207662.89,
'loss': 101241676000.0,
'prediction/mean': 211164.14,
'global_step': 20000}
In [101]:
# Step 6 - Prediction
predict_input_func = tf.estimator.inputs.pandas_input_fn(x=X_test,batch_size=10,num_epochs=1,shuffle=False)
pred_gen = model.predict(predict_input_func)
predictions = list(pred_gen)
predictions
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\LUCKY_~1\AppData\Local\Temp\tmpu3v2b8rd\model.ckpt-20000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op.
Out[101]:
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RMSE¶
In [102]:
final_preds = []
for pred in predictions:
final_preds.append(pred['predictions'])
from sklearn.metrics import mean_squared_error
In [103]:
mean_squared_error(y_test,final_preds) ** 0.5
Out[103]:
100683.9386679309
In [104]:
## Our Mean Squared error should be mean value of medianHouseValue Column - Which is 20000 from descibe()
Tensorflow Classification Exercise¶
We'll be working with some California Census Data, we'll be trying to use various features of an individual to predict what class of income they belogn in (>50k or <=50k).
Here is some information about the data:
| Column Name | Type | Description |
|---|---|---|
| age | Continuous | The age of the individual |
| workclass | Categorical | The type of employer the individual has (government, military, private, etc.). |
| fnlwgt | Continuous | The number of people the census takers believe that observation represents (sample weight). This variable will not be used. |
| education | Categorical | The highest level of education achieved for that individual. |
| education_num | Continuous | The highest level of education in numerical form. |
| marital_status | Categorical | Marital status of the individual. |
| occupation | Categorical | The occupation of the individual. |
| relationship | Categorical | Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. |
| race | Categorical | White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. |
| gender | Categorical | Female, Male. |
| capital_gain | Continuous | Capital gains recorded. |
| capital_loss | Continuous | Capital Losses recorded. |
| hours_per_week | Continuous | Hours worked per week. |
| native_country | Categorical | Country of origin of the individual. |
| income | Categorical | ">50K" or "<=50K", meaning whether the person makes more than \$50,000 annually. |
In [111]:
# Import Dataset
import pandas as pd
census = pd.read_csv(r'C:\Users\Lucky_Rathod\Desktop\Udemy Courses\Complete Tensorflow\Original Notes\Tensorflow-Bootcamp-master\02-TensorFlow-Basics\census_data.csv')
census.head()
Out[111]:
| age | workclass | education | education_num | marital_status | occupation | relationship | race | gender | capital_gain | capital_loss | hours_per_week | native_country | income_bracket | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 39 | State-gov | Bachelors | 13 | Never-married | Adm-clerical | Not-in-family | White | Male | 2174 | 0 | 40 | United-States | <=50K |
| 1 | 50 | Self-emp-not-inc | Bachelors | 13 | Married-civ-spouse | Exec-managerial | Husband | White | Male | 0 | 0 | 13 | United-States | <=50K |
| 2 | 38 | Private | HS-grad | 9 | Divorced | Handlers-cleaners | Not-in-family | White | Male | 0 | 0 | 40 | United-States | <=50K |
| 3 | 53 | Private | 11th | 7 | Married-civ-spouse | Handlers-cleaners | Husband | Black | Male | 0 | 0 | 40 | United-States | <=50K |
| 4 | 28 | Private | Bachelors | 13 | Married-civ-spouse | Prof-specialty | Wife | Black | Female | 0 | 0 | 40 | Cuba | <=50K |
In [112]:
#Convert label column to 0 and 1 instead of strings
census['income_bracket'].unique()
Out[112]:
array([' <=50K', ' >50K'], dtype=object)
In [113]:
def label_fix(label):
if label == ' <=50K':
return 0
else:
return 1
census['income_bracket'] = census['income_bracket'].apply(label_fix)
census.head()
Out[113]:
| age | workclass | education | education_num | marital_status | occupation | relationship | race | gender | capital_gain | capital_loss | hours_per_week | native_country | income_bracket | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 39 | State-gov | Bachelors | 13 | Never-married | Adm-clerical | Not-in-family | White | Male | 2174 | 0 | 40 | United-States | 0 |
| 1 | 50 | Self-emp-not-inc | Bachelors | 13 | Married-civ-spouse | Exec-managerial | Husband | White | Male | 0 | 0 | 13 | United-States | 0 |
| 2 | 38 | Private | HS-grad | 9 | Divorced | Handlers-cleaners | Not-in-family | White | Male | 0 | 0 | 40 | United-States | 0 |
| 3 | 53 | Private | 11th | 7 | Married-civ-spouse | Handlers-cleaners | Husband | Black | Male | 0 | 0 | 40 | United-States | 0 |
| 4 | 28 | Private | Bachelors | 13 | Married-civ-spouse | Prof-specialty | Wife | Black | Female | 0 | 0 | 40 | Cuba | 0 |
In [114]:
# Train Test Split
from sklearn.model_selection import train_test_split
x_data = census.drop('income_bracket',axis=1)
y_labels = census['income_bracket']
X_train, X_test, y_train, y_test = train_test_split(x_data, y_labels, test_size=0.30, random_state=101)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
(22792, 13) (9769, 13) (22792,) (9769,)
In [115]:
census.columns
Out[115]:
Index(['age', 'workclass', 'education', 'education_num', 'marital_status',
'occupation', 'relationship', 'race', 'gender', 'capital_gain',
'capital_loss', 'hours_per_week', 'native_country', 'income_bracket'],
dtype='object')
In [116]:
import tensorflow as tf
In [117]:
# Step 1 - List of Feature Columns
## Categorical
gender = tf.feature_column.categorical_column_with_vocabulary_list("gender", ["Female", "Male"])
occupation = tf.feature_column.categorical_column_with_hash_bucket("occupation", hash_bucket_size=1000)
marital_status = tf.feature_column.categorical_column_with_hash_bucket("marital_status", hash_bucket_size=1000)
relationship = tf.feature_column.categorical_column_with_hash_bucket("relationship", hash_bucket_size=1000)
education = tf.feature_column.categorical_column_with_hash_bucket("education", hash_bucket_size=1000)
workclass = tf.feature_column.categorical_column_with_hash_bucket("workclass", hash_bucket_size=1000)
native_country = tf.feature_column.categorical_column_with_hash_bucket("native_country", hash_bucket_size=1000)
## Numerical
age = tf.feature_column.numeric_column("age")
education_num = tf.feature_column.numeric_column("education_num")
capital_gain = tf.feature_column.numeric_column("capital_gain")
capital_loss = tf.feature_column.numeric_column("capital_loss")
hours_per_week = tf.feature_column.numeric_column("hours_per_week")
feat_cols = [gender,occupation,marital_status,relationship,education,workclass,native_country,
age,education_num,capital_gain,capital_loss,hours_per_week]
Create a LinearClassifier.(If you want to use a DNNClassifier, keep in mind you'll need to create embedded columns out of the cateogrical feature that use strings, check out the previous lecture on this for more info.)
In [118]:
# Step 2 - Input Function
#We are creating Linear Classifier . No need of epochs .
#If we used Neural Network then epochs are used
input_func = tf.estimator.inputs.pandas_input_fn(x=X_train,y=y_train,batch_size=100,num_epochs=None,shuffle=True)
In [119]:
# Step 3 - Create Model
model = tf.estimator.LinearClassifier(feature_columns=feat_cols)
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: C:\Users\LUCKY_~1\AppData\Local\Temp\tmp5mfeelf1
INFO:tensorflow:Using config: {'_model_dir': 'C:\\Users\\LUCKY_~1\\AppData\\Local\\Temp\\tmp5mfeelf1', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x0000025F9A873B00>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
In [120]:
# Step 4 - Train Model
model.train(input_fn=input_func,steps=10000)
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Saving checkpoints for 0 into C:\Users\LUCKY_~1\AppData\Local\Temp\tmp5mfeelf1\model.ckpt. INFO:tensorflow:loss = 69.31472, step = 0 INFO:tensorflow:global_step/sec: 16.6751 INFO:tensorflow:loss = 589.50446, step = 100 (5.993 sec) INFO:tensorflow:global_step/sec: 38.7645 INFO:tensorflow:loss = 231.88809, step = 200 (2.582 sec) INFO:tensorflow:global_step/sec: 40.3173 INFO:tensorflow:loss = 300.99094, step = 300 (2.479 sec) INFO:tensorflow:global_step/sec: 39.5892 INFO:tensorflow:loss = 212.03342, step = 400 (2.526 sec) INFO:tensorflow:global_step/sec: 40.2857 INFO:tensorflow:loss = 186.92662, step = 500 (2.482 sec) INFO:tensorflow:global_step/sec: 43.8948 INFO:tensorflow:loss = 122.53108, step = 600 (2.278 sec) INFO:tensorflow:global_step/sec: 45.3296 INFO:tensorflow:loss = 175.69759, step = 700 (2.206 sec) INFO:tensorflow:global_step/sec: 44.5603 INFO:tensorflow:loss = 212.37369, step = 800 (2.244 sec) INFO:tensorflow:global_step/sec: 42.3427 INFO:tensorflow:loss = 170.6681, step = 900 (2.362 sec) INFO:tensorflow:global_step/sec: 39.1976 INFO:tensorflow:loss = 222.10216, step = 1000 (2.551 sec) INFO:tensorflow:global_step/sec: 41.1519 INFO:tensorflow:loss = 73.21914, step = 1100 (2.429 sec) INFO:tensorflow:global_step/sec: 45.889 INFO:tensorflow:loss = 86.231415, step = 1200 (2.179 sec) INFO:tensorflow:global_step/sec: 42.9596 INFO:tensorflow:loss = 28.545326, step = 1300 (2.328 sec) INFO:tensorflow:global_step/sec: 46.0154 INFO:tensorflow:loss = 46.53218, step = 1400 (2.173 sec) INFO:tensorflow:global_step/sec: 46.5279 INFO:tensorflow:loss = 213.45363, step = 1500 (2.152 sec) INFO:tensorflow:global_step/sec: 42.2179 INFO:tensorflow:loss = 47.628304, step = 1600 (2.368 sec) INFO:tensorflow:global_step/sec: 41.9179 INFO:tensorflow:loss = 43.059803, step = 1700 (2.385 sec) INFO:tensorflow:global_step/sec: 41.7434 INFO:tensorflow:loss = 82.50764, step = 1800 (2.395 sec) INFO:tensorflow:global_step/sec: 39.213 INFO:tensorflow:loss = 304.03497, step = 1900 (2.550 sec) INFO:tensorflow:global_step/sec: 39.6942 INFO:tensorflow:loss = 22.893238, step = 2000 (2.520 sec) INFO:tensorflow:global_step/sec: 37.2881 INFO:tensorflow:loss = 161.61124, step = 2100 (2.682 sec) INFO:tensorflow:global_step/sec: 41.726 INFO:tensorflow:loss = 309.2141, step = 2200 (2.397 sec) INFO:tensorflow:global_step/sec: 43.2561 INFO:tensorflow:loss = 170.26103, step = 2300 (2.312 sec) INFO:tensorflow:global_step/sec: 43.4058 INFO:tensorflow:loss = 44.137043, step = 2400 (2.303 sec) INFO:tensorflow:global_step/sec: 46.5063 INFO:tensorflow:loss = 112.16445, step = 2500 (2.151 sec) INFO:tensorflow:global_step/sec: 37.3158 INFO:tensorflow:loss = 49.935127, step = 2600 (2.680 sec) INFO:tensorflow:global_step/sec: 40.059 INFO:tensorflow:loss = 18.180132, step = 2700 (2.496 sec) INFO:tensorflow:global_step/sec: 39.9632 INFO:tensorflow:loss = 171.93657, step = 2800 (2.502 sec) INFO:tensorflow:global_step/sec: 41.0092 INFO:tensorflow:loss = 26.81203, step = 2900 (2.439 sec) INFO:tensorflow:global_step/sec: 43.0333 INFO:tensorflow:loss = 23.187828, step = 3000 (2.323 sec) INFO:tensorflow:global_step/sec: 45.9521 INFO:tensorflow:loss = 104.57145, step = 3100 (2.176 sec) INFO:tensorflow:global_step/sec: 47.0298 INFO:tensorflow:loss = 232.27274, step = 3200 (2.126 sec) INFO:tensorflow:global_step/sec: 38.7732 INFO:tensorflow:loss = 35.576126, step = 3300 (2.579 sec) INFO:tensorflow:global_step/sec: 41.6913 INFO:tensorflow:loss = 40.711258, step = 3400 (2.398 sec) INFO:tensorflow:global_step/sec: 43.6135 INFO:tensorflow:loss = 124.6991, step = 3500 (2.296 sec) INFO:tensorflow:global_step/sec: 34.791 INFO:tensorflow:loss = 25.14659, step = 3600 (2.872 sec) INFO:tensorflow:global_step/sec: 45.1452 INFO:tensorflow:loss = 40.91261, step = 3700 (2.214 sec) INFO:tensorflow:global_step/sec: 47.5878 INFO:tensorflow:loss = 86.6861, step = 3800 (2.102 sec) INFO:tensorflow:global_step/sec: 47.4527 INFO:tensorflow:loss = 103.65815, step = 3900 (2.106 sec) INFO:tensorflow:global_step/sec: 47.1625 INFO:tensorflow:loss = 60.53021, step = 4000 (2.120 sec) INFO:tensorflow:global_step/sec: 47.3407 INFO:tensorflow:loss = 54.701107, step = 4100 (2.112 sec) INFO:tensorflow:global_step/sec: 44.9026 INFO:tensorflow:loss = 39.662384, step = 4200 (2.227 sec) INFO:tensorflow:global_step/sec: 42.8861 INFO:tensorflow:loss = 47.54911, step = 4300 (2.332 sec) INFO:tensorflow:global_step/sec: 42.667 INFO:tensorflow:loss = 66.552536, step = 4400 (2.345 sec) INFO:tensorflow:global_step/sec: 45.4728 INFO:tensorflow:loss = 39.841885, step = 4500 (2.198 sec) INFO:tensorflow:global_step/sec: 45.0843 INFO:tensorflow:loss = 552.8604, step = 4600 (2.283 sec) INFO:tensorflow:global_step/sec: 44.6626 INFO:tensorflow:loss = 41.92741, step = 4700 (2.175 sec) INFO:tensorflow:global_step/sec: 44.9429 INFO:tensorflow:loss = 45.310738, step = 4800 (2.224 sec) INFO:tensorflow:global_step/sec: 43.6515 INFO:tensorflow:loss = 108.994026, step = 4900 (2.291 sec) INFO:tensorflow:global_step/sec: 35.5559 INFO:tensorflow:loss = 30.813564, step = 5000 (2.813 sec) INFO:tensorflow:global_step/sec: 41.0933 INFO:tensorflow:loss = 43.23401, step = 5100 (2.432 sec) INFO:tensorflow:global_step/sec: 45.7008 INFO:tensorflow:loss = 32.555412, step = 5200 (2.189 sec) INFO:tensorflow:global_step/sec: 38.7732 INFO:tensorflow:loss = 41.82456, step = 5300 (2.580 sec) INFO:tensorflow:global_step/sec: 38.0234 INFO:tensorflow:loss = 24.526789, step = 5400 (2.628 sec) INFO:tensorflow:global_step/sec: 45.9732 INFO:tensorflow:loss = 31.335249, step = 5500 (2.176 sec) INFO:tensorflow:global_step/sec: 40.3329 INFO:tensorflow:loss = 282.25577, step = 5600 (2.493 sec) INFO:tensorflow:global_step/sec: 36.0934 INFO:tensorflow:loss = 30.647985, step = 5700 (2.757 sec) INFO:tensorflow:global_step/sec: 42.1647 INFO:tensorflow:loss = 81.62087, step = 5800 (2.372 sec) INFO:tensorflow:global_step/sec: 35.5558 INFO:tensorflow:loss = 52.30932, step = 5900 (2.812 sec) INFO:tensorflow:global_step/sec: 41.0428 INFO:tensorflow:loss = 237.06848, step = 6000 (2.435 sec) INFO:tensorflow:global_step/sec: 46.2916 INFO:tensorflow:loss = 44.07435, step = 6100 (2.160 sec) INFO:tensorflow:global_step/sec: 47.2292 INFO:tensorflow:loss = 209.40611, step = 6200 (2.118 sec) INFO:tensorflow:global_step/sec: 40.9088 INFO:tensorflow:loss = 52.555267, step = 6300 (2.444 sec) INFO:tensorflow:global_step/sec: 35.6912 INFO:tensorflow:loss = 96.94634, step = 6400 (2.804 sec) INFO:tensorflow:global_step/sec: 40.9634 INFO:tensorflow:loss = 86.91816, step = 6500 (2.438 sec) INFO:tensorflow:global_step/sec: 40.9985 INFO:tensorflow:loss = 53.56658, step = 6600 (2.439 sec) INFO:tensorflow:global_step/sec: 41.9998 INFO:tensorflow:loss = 23.847078, step = 6700 (2.382 sec) INFO:tensorflow:global_step/sec: 37.9031 INFO:tensorflow:loss = 34.13314, step = 6800 (2.638 sec) INFO:tensorflow:global_step/sec: 40.2774 INFO:tensorflow:loss = 33.339092, step = 6900 (2.486 sec) INFO:tensorflow:global_step/sec: 38.4666 INFO:tensorflow:loss = 24.430107, step = 7000 (2.598 sec) INFO:tensorflow:global_step/sec: 44.3222 INFO:tensorflow:loss = 67.70282, step = 7100 (2.255 sec) INFO:tensorflow:global_step/sec: 43.0072 INFO:tensorflow:loss = 124.31525, step = 7200 (2.326 sec) INFO:tensorflow:global_step/sec: 44.603 INFO:tensorflow:loss = 24.394337, step = 7300 (2.240 sec) INFO:tensorflow:global_step/sec: 47.1847 INFO:tensorflow:loss = 68.15183, step = 7400 (2.119 sec) INFO:tensorflow:global_step/sec: 43.5 INFO:tensorflow:loss = 28.481287, step = 7500 (2.299 sec) INFO:tensorflow:global_step/sec: 43.1073 INFO:tensorflow:loss = 36.89214, step = 7600 (2.321 sec) INFO:tensorflow:global_step/sec: 39.0602 INFO:tensorflow:loss = 55.18079, step = 7700 (2.560 sec) INFO:tensorflow:global_step/sec: 45.9311 INFO:tensorflow:loss = 148.01093, step = 7800 (2.177 sec) INFO:tensorflow:global_step/sec: 39.5533 INFO:tensorflow:loss = 128.79019, step = 7900 (2.528 sec) INFO:tensorflow:global_step/sec: 40.7756 INFO:tensorflow:loss = 51.798702, step = 8000 (2.452 sec) INFO:tensorflow:global_step/sec: 45.1454 INFO:tensorflow:loss = 30.1587, step = 8100 (2.215 sec) INFO:tensorflow:global_step/sec: 42.1292 INFO:tensorflow:loss = 49.626106, step = 8200 (2.375 sec) INFO:tensorflow:global_step/sec: 34.4918 INFO:tensorflow:loss = 36.65219, step = 8300 (2.898 sec) INFO:tensorflow:global_step/sec: 43.1444 INFO:tensorflow:loss = 32.575287, step = 8400 (2.318 sec) INFO:tensorflow:global_step/sec: 45.2163 INFO:tensorflow:loss = 23.758951, step = 8500 (2.212 sec) INFO:tensorflow:global_step/sec: 47.1403 INFO:tensorflow:loss = 84.72438, step = 8600 (2.121 sec) INFO:tensorflow:global_step/sec: 43.7086 INFO:tensorflow:loss = 32.607918, step = 8700 (2.288 sec) INFO:tensorflow:global_step/sec: 46.8321 INFO:tensorflow:loss = 151.02206, step = 8800 (2.134 sec) INFO:tensorflow:global_step/sec: 46.8759 INFO:tensorflow:loss = 39.59335, step = 8900 (2.133 sec) INFO:tensorflow:global_step/sec: 40.8256 INFO:tensorflow:loss = 47.983994, step = 9000 (2.451 sec) INFO:tensorflow:global_step/sec: 37.3714 INFO:tensorflow:loss = 35.97558, step = 9100 (2.675 sec) INFO:tensorflow:global_step/sec: 37.0693 INFO:tensorflow:loss = 58.426315, step = 9200 (2.698 sec) INFO:tensorflow:global_step/sec: 35.4166 INFO:tensorflow:loss = 28.698076, step = 9300 (2.824 sec) INFO:tensorflow:global_step/sec: 35.0507 INFO:tensorflow:loss = 39.49119, step = 9400 (2.853 sec) INFO:tensorflow:global_step/sec: 47.3019 INFO:tensorflow:loss = 40.091248, step = 9500 (2.114 sec) INFO:tensorflow:global_step/sec: 44.7824 INFO:tensorflow:loss = 21.936813, step = 9600 (2.267 sec) INFO:tensorflow:global_step/sec: 44.9832 INFO:tensorflow:loss = 139.56915, step = 9700 (2.188 sec) INFO:tensorflow:global_step/sec: 47.2515 INFO:tensorflow:loss = 32.627037, step = 9800 (2.116 sec) INFO:tensorflow:global_step/sec: 45.9522 INFO:tensorflow:loss = 32.65236, step = 9900 (2.180 sec) INFO:tensorflow:Saving checkpoints for 10000 into C:\Users\LUCKY_~1\AppData\Local\Temp\tmp5mfeelf1\model.ckpt. INFO:tensorflow:Loss for final step: 46.943207.
Out[120]:
<tensorflow_estimator.python.estimator.canned.linear.LinearClassifier at 0x25f9a873780>
In [121]:
# Step 5 - Evaluate
eval_input_func = tf.estimator.inputs.pandas_input_fn(x = X_test,y=y_test,batch_size=10,num_epochs=1,shuffle=False)
evalmetrics = model.evaluate(input_fn=eval_input_func)
evalmetrics
INFO:tensorflow:Calling model_fn. WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead. WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2020-08-20T16:56:00Z INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\LUCKY_~1\AppData\Local\Temp\tmp5mfeelf1\model.ckpt-10000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Finished evaluation at 2020-08-20-16:56:27 INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.8379568, accuracy_baseline = 0.7611833, auc = 0.8863338, auc_precision_recall = 0.6880697, average_loss = 0.536477, global_step = 10000, label/mean = 0.23881666, loss = 5.364221, precision = 0.67490673, prediction/mean = 0.25514737, recall = 0.62023145 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: C:\Users\LUCKY_~1\AppData\Local\Temp\tmp5mfeelf1\model.ckpt-10000
Out[121]:
{'accuracy': 0.8379568,
'accuracy_baseline': 0.7611833,
'auc': 0.8863338,
'auc_precision_recall': 0.6880697,
'average_loss': 0.536477,
'label/mean': 0.23881666,
'loss': 5.364221,
'precision': 0.67490673,
'prediction/mean': 0.25514737,
'recall': 0.62023145,
'global_step': 10000}
In [122]:
# Step 6 - Prediction
pred_fn = tf.estimator.inputs.pandas_input_fn(x=X_test,batch_size=len(X_test),shuffle=False)
predictions = list(model.predict(input_fn=pred_fn))
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\LUCKY_~1\AppData\Local\Temp\tmp5mfeelf1\model.ckpt-10000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op.
In [123]:
predictions
Out[123]:
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'logistic': array([0.2932865], dtype=float32),
'probabilities': array([0.70671356, 0.29328647], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-8.976079], dtype=float32),
'logistic': array([0.00012636], dtype=float32),
'probabilities': array([9.9987364e-01, 1.2638152e-04], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-0.4255914], dtype=float32),
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'probabilities': array([0.60482043, 0.39517954], dtype=float32),
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'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-10.56875], dtype=float32),
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'classes': array([b'0'], dtype=object),
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{'logits': array([19.948484], dtype=float32),
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{'logits': array([-4.098391], dtype=float32),
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{'logits': array([-5.722513], dtype=float32),
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{'logits': array([-6.2266316], dtype=float32),
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{'logits': array([-2.3318813], dtype=float32),
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{'logits': array([-0.22503215], dtype=float32),
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'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-3.1331925], dtype=float32),
'logistic': array([0.04175863], dtype=float32),
'probabilities': array([0.9582413 , 0.04175867], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-1.2318757], dtype=float32),
'logistic': array([0.2258533], dtype=float32),
'probabilities': array([0.7741467 , 0.22585332], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([22.750547], dtype=float32),
'logistic': array([1.], dtype=float32),
'probabilities': array([1.3169303e-10, 1.0000000e+00], dtype=float32),
'class_ids': array([1], dtype=int64),
'classes': array([b'1'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-0.41755974], dtype=float32),
'logistic': array([0.39710084], dtype=float32),
'probabilities': array([0.6028992 , 0.39710084], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-9.912064], dtype=float32),
'logistic': array([4.9561262e-05], dtype=float32),
'probabilities': array([9.999504e-01, 4.957057e-05], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-0.06452805], dtype=float32),
'logistic': array([0.48387358], dtype=float32),
'probabilities': array([0.51612645, 0.4838736 ], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-3.8345418], dtype=float32),
'logistic': array([0.02115408], dtype=float32),
'probabilities': array([0.97884595, 0.02115407], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-6.3275723], dtype=float32),
'logistic': array([0.00178322], dtype=float32),
'probabilities': array([0.9982168 , 0.00178318], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-0.8120525], dtype=float32),
'logistic': array([0.30745333], dtype=float32),
'probabilities': array([0.6925467, 0.3074533], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-4.2475786], dtype=float32),
'logistic': array([0.01409721], dtype=float32),
'probabilities': array([0.9859028 , 0.01409724], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-6.748852], dtype=float32),
'logistic': array([0.00117084], dtype=float32),
'probabilities': array([0.9988292 , 0.00117085], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([1.0790389], dtype=float32),
'logistic': array([0.7463121], dtype=float32),
'probabilities': array([0.25368795, 0.7463121 ], dtype=float32),
'class_ids': array([1], dtype=int64),
'classes': array([b'1'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-3.0252292], dtype=float32),
'logistic': array([0.04629904], dtype=float32),
'probabilities': array([0.953701 , 0.04629903], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-1.6947412], dtype=float32),
'logistic': array([0.15515336], dtype=float32),
'probabilities': array([0.8448466 , 0.15515333], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-0.29562014], dtype=float32),
'logistic': array([0.42662853], dtype=float32),
'probabilities': array([0.57337147, 0.4266285 ], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-0.07728177], dtype=float32),
'logistic': array([0.48068917], dtype=float32),
'probabilities': array([0.51931083, 0.48068917], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-1.0239639], dtype=float32),
'logistic': array([0.264256], dtype=float32),
'probabilities': array([0.735744, 0.264256], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-2.7318428], dtype=float32),
'logistic': array([0.0611203], dtype=float32),
'probabilities': array([0.93887967, 0.06112033], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-3.5308409], dtype=float32),
'logistic': array([0.02844736], dtype=float32),
'probabilities': array([0.97155267, 0.02844734], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-3.60064], dtype=float32),
'logistic': array([0.02658042], dtype=float32),
'probabilities': array([0.9734196 , 0.02658043], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-8.188843], dtype=float32),
'logistic': array([0.0002777], dtype=float32),
'probabilities': array([9.997223e-01, 2.776580e-04], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-1.7671819], dtype=float32),
'logistic': array([0.14589313], dtype=float32),
'probabilities': array([0.85410684, 0.14589314], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-9.913489], dtype=float32),
'logistic': array([4.9471855e-05], dtype=float32),
'probabilities': array([9.9995053e-01, 4.9499955e-05], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-3.6170497], dtype=float32),
'logistic': array([0.02615914], dtype=float32),
'probabilities': array([0.9738409 , 0.02615913], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-3.7379825], dtype=float32),
'logistic': array([0.0232487], dtype=float32),
'probabilities': array([0.9767513 , 0.02324871], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-6.061199], dtype=float32),
'logistic': array([0.00232616], dtype=float32),
'probabilities': array([0.9976738 , 0.00232618], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-2.0675278], dtype=float32),
'logistic': array([0.11229321], dtype=float32),
'probabilities': array([0.88770676, 0.11229324], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([45.95173], dtype=float32),
'logistic': array([1.], dtype=float32),
'probabilities': array([1.1051411e-20, 1.0000000e+00], dtype=float32),
'class_ids': array([1], dtype=int64),
'classes': array([b'1'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-7.4773693], dtype=float32),
'logistic': array([0.00056541], dtype=float32),
'probabilities': array([9.9943453e-01, 5.6542386e-04], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-6.388591], dtype=float32),
'logistic': array([0.00167781], dtype=float32),
'probabilities': array([0.9983222, 0.0016778], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-9.6781], dtype=float32),
'logistic': array([6.264448e-05], dtype=float32),
'probabilities': array([9.9993742e-01, 6.2636514e-05], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-3.0670137], dtype=float32),
'logistic': array([0.04448861], dtype=float32),
'probabilities': array([0.95551145, 0.0444886 ], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-8.930611], dtype=float32),
'logistic': array([0.00013223], dtype=float32),
'probabilities': array([9.9986768e-01, 1.3225972e-04], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-10.832341], dtype=float32),
'logistic': array([1.975894e-05], dtype=float32),
'probabilities': array([9.9998021e-01, 1.9749921e-05], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([0.26210386], dtype=float32),
'logistic': array([0.56515336], dtype=float32),
'probabilities': array([0.43484658, 0.5651534 ], dtype=float32),
'class_ids': array([1], dtype=int64),
'classes': array([b'1'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-4.713229], dtype=float32),
'logistic': array([0.00889587], dtype=float32),
'probabilities': array([0.9911041, 0.0088959], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
{'logits': array([-3.5634112], dtype=float32),
'logistic': array([0.02756089], dtype=float32),
'probabilities': array([0.9724391 , 0.02756085], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)},
...]
In [124]:
predictions[0]
Out[124]:
{'logits': array([-0.87947553], dtype=float32),
'logistic': array([0.2932865], dtype=float32),
'probabilities': array([0.70671356, 0.29328647], dtype=float32),
'class_ids': array([0], dtype=int64),
'classes': array([b'0'], dtype=object),
'all_class_ids': array([0, 1]),
'all_classes': array([b'0', b'1'], dtype=object)}
In [125]:
final_preds = []
for pred in predictions:
final_preds.append(pred['class_ids'][0])
In [126]:
final_preds[:10]
Out[126]:
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
In [127]:
from sklearn.metrics import classification_report
In [128]:
print(classification_report(y_test,final_preds))
precision recall f1-score support
0 0.88 0.91 0.89 7436
1 0.67 0.62 0.65 2333
accuracy 0.84 9769
macro avg 0.78 0.76 0.77 9769
weighted avg 0.83 0.84 0.84 9769
Saving and Restoring Models¶
In [130]:
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(101)
tf.set_random_seed(101)
Full Network Example¶
Let's work on a regression example, we are trying to solve a very simple equation:
y = mx + b
y will be the y_labels and x is the x_data. We are trying to figure out the slope and the intercept for the line that best fits our data!
In [131]:
x_data = np.linspace(0,10,10) + np.random.uniform(-1.5,1.5,10)
y_label = np.linspace(0,10,10) + np.random.uniform(-1.5,1.5,10)
plt.plot(x_data,y_label,'*');
In [132]:
m = tf.Variable(0.39)
b = tf.Variable(0.2)
# Cost Function
error = tf.reduce_mean(y_label - (m*x_data+b))
# Optimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
train = optimizer.minimize(error)
init = tf.global_variables_initializer()
Saving Model¶
In [133]:
saver = tf.train.Saver()
# Create a session and run
with tf.Session() as sess:
sess.run(init)
epochs = 100
for i in range(epochs):
sess.run(train)
# Fetch Back Results
final_slope , final_intercept = sess.run([m,b])
# ONCE YOU ARE DONE
# GO AHEAD AND SAVE IT!
# Make sure to provide a directory for it to make or go to. May get errors otherwise
#saver.save(sess,'models/my_first_model.ckpt')
saver.save(sess,r'C:\Users\Lucky_Rathod\Desktop\Udemy Courses\Complete Tensorflow\Original Notes\Tensorflow-Bootcamp-master\02-TensorFlow-Basics\new_models_lucky\my_second_model.ckpt')
In [134]:
# Evaluate Results
x_test = np.linspace(-1,11,10)
y_pred_plot = final_slope*x_test + final_intercept
plt.plot(x_test,y_pred_plot,'r')
plt.plot(x_data,y_label,'*')
Out[134]:
[<matplotlib.lines.Line2D at 0x25fa17a4be0>]
Loading Model¶
In [135]:
with tf.Session() as sess:
# Restore the model
saver.restore(sess,r'C:\Users\Lucky_Rathod\Desktop\Udemy Courses\Complete Tensorflow\Original Notes\Tensorflow-Bootcamp-master\02-TensorFlow-Basics\new_models_lucky\my_second_model.ckpt')
# Fetch Back Results
restored_slope , restored_intercept = sess.run([m,b])
INFO:tensorflow:Restoring parameters from C:\Users\Lucky_Rathod\Desktop\Udemy Courses\Complete Tensorflow\Original Notes\Tensorflow-Bootcamp-master\02-TensorFlow-Basics\new_models_lucky\my_second_model.ckpt
In [136]:
x_test = np.linspace(-1,11,10)
y_pred_plot = restored_slope*x_test + restored_intercept
plt.plot(x_test,y_pred_plot,'r')
plt.plot(x_data,y_label,'*')
Out[136]:
[<matplotlib.lines.Line2D at 0x25fa2171128>]
Completed¶
In [ ]: